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Market Synopsis According to MRFR, the Sales Performance Management Market Growth is expected to grow at a CAGR of 16.6% during the forecast period and reach USD 9.34 billion by 2027. Market Highlights The demand for sales performance management has begun to develop over the past few years and a substantial growth rate is anticipated in the coming years. Sales performance management supports retail, banking and financial services, manufacturing, IT and telecommunication, and other sectors in their revenue fuctionality. The major factors contributing to the growth of the market are the growing adoption of Bring-your-own-devices (BYOD) by companies and the the demand for improved channel and efficiency of sales. The other considerations are the need for metrics based sales tools, and the need for process experience and channel performance indication. The limited knowledge of the tools for sales performance management is proving a constraint to this market. An opportunity for such solutions is the increasing incorporation of integrated platforms/products into enterprise sales functions. Get a Free Sample @ https //www.marketresearchfuture.com/sample_request/7476 Segmentation The global market for sales performance management is segmented into part, mode of delivery, size of organization, vertical and region / country. The global sales performance management is segmented by part into products and services. However, the Services section is divided into incentive compensation management, geographic management , business planning and tracking, market analytics and others. Furthermore, the Services segment is divided into consulting, implementation , training and support and management services. The global monitoring of sales output is segmented into on-premises and cloud by delivery mode. The global sales performance management is segmented by organizational size into small and medium-sized enterprises ( SMEs) and large corporations. The global sales performance management is segmented vertically into telecommunications and IT; retail; health and pharmaceutical; manufacturing; banking, financial and insurance (BFSI); travel and hospitality; transport and logistics; and others. Regional analysis Sales performance management business regional research is examined for North America, Europe , Asia-Pacific and the rest of the world (including the Middle East, Africa , and Latin America). North America is projected to have the largest market share during the forecast period amongst the regions listed. The high demand for sales performance management software from technologically advanced industries such as BFSI and Telecom and IT in North America is expected to raise the market size. Europe is expected to follow North America, in terms of market size. The increasing demand for better distribution channel and company results in Europe, as well as the growth of numerous US-based sales performance management software companies in Europe, are key reasons for Europe s second-largest market share. Asia Pacific is projected to have the highest growth rate as demand is expected to increase by 16.2 per cent over the 2018 to 2023 forecast period above the global CAGR. Emerging countries such as India and China are increasingly driving the sales performance management market across various sectors including BFSI, IT and Telecom, manufacturing, and retail. Rest of the World — Latin America and Middle East Africa are projected to give vendors various opportunities as sales success management systems are yet to be implemented in most countries. Competitive Dynamics Some of the major players are Salesforce.Com, Inc. (US), SAP AG (Germany), Synygy, Inc. (US), Xactly Corporation (US), Oracle Corporation (US), IBM Corporation (US), Callidus Software, Inc. (US), Microsoft Corporation (US), Axtria Inc. (US), Optymyze (US), Iconixx Corporation (US), Nice Systems Ltd. (US), Anaplan, Inc. (India), and Performio Solutions Inc. (US). The major strategies adopted by most of the players are collaborations, partnerships and agreements, and new product releases. Browse Complete Report @ https //www.marketresearchfuture.com/reports/sales-performance-management-market-7476 Table of Contents 1 Executive Summary 2 Scope of The Report 2.1 Market Definition 2.2 Scope of The Study 2.2.1 Research Objectives 2.2.2 Assumptions Limitations 2.3 Market Structure Continued… Similar Report B2B Telecommunication Market Information by Solution (Unified Communication and Collaboration), Deployment (Fixed, Mobile), Organization Size (Large, Enterprise), Application (Industrial, Commercial) and regions Trending #MRFR Report** https //ictmrfr.blogspot.com/2022/04/geofencing-market-companies-growth-with.html https //blogfreely.net/pranali004/telecom-expense-management-market-size-impressive-cagr-changing-business-scope https //postheaven.net/pranali004/financial-app-industry-impressive-cagr-changing-business-needs-scope-of https //market-research-future.tribe.so/post/openstack-service-market-research-impressive-cagr-changing-scope-of-current--6263de46791566c10c79891e https //www.scutify.com/articles/2022-04-24-infrastructure-as-a-service-industry-cagr-changing-business-scope-of-current-and-future-industry- About Market Research Future At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research Consulting Services. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com
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Market Scenario The Geofencing Market is projected to grow at a CAGR of 27% during the forecast period. As per the geofencing market research report, the global market for geofencing is projected to grow swiftly by US$2,387 million by 2023. According to analysts, increasing demand for geofencing solutions as well as increasing demand for location based applications will drive the market growth during the forecast period. The geofencing market research report offers a comprehensive analysis of the global geofencing market and its deployment, device type, organization size, vertical, and component segments. The limited technological advancements along with lack of awareness are the elements that could influence the geofencing market advancement throughout the forecast period. The geofencing market research report by expert analysts is developed to assist organizations in the geofencing market. Various factors are fuelling the global geofencing market demand. As per the recent MRFR market estimates, such factors include the increasing need for location based applications among consumers, increase in the use of spatial data and analytical tools, ease of integration and deployment of geofencing solutions, the penetration of new technologies, and the rise in the use of spatial data and analytical tools. The additional factors adding market growth include the growth of competitive intelligence, rise in business intelligence, the need to track the marketing activities of competitors, increasing technological advances to maintain the security and safety majors for the organization, demand for geofencing marketing, increasing use during COVID-19 pandemic, and growing application in certain sectors like child location service, telematics, and human resources. On the contrary, privacy and legal concerns, increasing awareness about safety and security among customers, high deployment cost, lack of knowledge, battery draining issues, and technological concerns related to devising monitoring may impede the global geofencing market growth over the forecast period. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/4490 Competitive Outlook The leading players profiled in the global geofencing market report include Swirl Networks Inc. (U.S.), Localytics (U.S.), GPSWOX, Ltd. (U.S.), Geomoby (Australia), Bluedot Innovation (U.S.), Esri (U.S.), Simpli.Fi Holdings Inc. (U.S.), Pulsate (U.S.), Thumbvista (U.S.), and Apple, INC. (U.S.), among others. The other players include SuccorfishM2M (U.K.), Visioglobe (France), Raveon Technologies (U.S.), Plot Projects (Netherlands), Urban Airship (US), Nisos Technologies (U.S.), MobiOcean (India), Maven Systems (India), LocationSmart (U.S.), InVisage (U.S.), Factual (U.S.), DreamOrbit (India), Mapcite (U.K.),and Mobinius Technologies (India), among others. Segmentation The global geofencing market has been segmented based on deployment, device type, organization size, vertical, and component. The market on the basis of device type, is segmented into fixed geofencing and mobile geofencing . The global market for geofencing is also covered based on organization size segment which is further split into SMEs and large enterprises. On the basis of verticals, the market for geofencing is segmented based on government, healthcare, manufacturing, media entertainment, retail, transportation, BFSI, and others. Additionally, the market on the basis of components, is segmented into solutions and services. Major elements such as lack of investment could obstruct the geofencing market growth. However, according to the geofencing market research report, rise in the use of spatial data along with growing use of analytical tools will propel growth throughout the forecast period. The geofencing market is set to register growth at a high CAGR owing to these key factors. The exploration of deployment, device type, organization size, vertical, and component segments along with regional markets has been given in the global geofencing market research report. The research analysts studying the geofencing market have put out market forecasts in the geofencing market research report in order to support geofencing market-based companies. The geofencing market research report provides an extensive understanding of the geofencing market based on the information and forecasts till 2023. Regional Analysis North America, Europe, Asia Pacific and the rest of the world regional market for geofencing are predominantly covered in the global geofencing market research report. Country-level geofencing markets spread across North America – the United States, Canada, and Mexico are also covered in the report. In South America – Brazil and other country-level geofencing markets are covered in the report. In Asia-Pacific (APAC) region, the country-level geofencing markets covered are Japan, India, China, and others. The geofencing market research report also explores the regional market for geofencing present in Europe in the United Kingdom, France, Italy, Spain, and Germany, etc. The geofencing market research report also covers regional markets from the rest of the world alongside geofencing markets of Africa and the Middle East. Industry News The introduction of its Geofencing software as an additional solution for dealers to achieve higher conversions was announced by ShopSmartAutos. An all-time high in online sales has been achieved by automotive shopping. COVID 19 moved young and old into the digital era rapidly. An early adapter to digital was the automotive market. Consumers, including their cars, have made the transition into completely trusted online shopping. In digital prospecting, lead generation businesses have proved to be the most productive, but the transition has fallen short. ShopSmartAutos enables the seller to buy leads that move directly to the VIN specific stock of the dealer from it s own search engine. Browse Full Report Details @ https //www.marketresearchfuture.com/reports/geofencing-market-4490 Table of Contents 1Executive Summary 2Scope of the Report 2.1Market Definition 2.2Scope of the Study 2.2.1Research objectives 2.2.2Assumptions Limitations 2.3Markets Structure Continued…. Read More** https //ictmrfr.blogspot.com/2022/03/pos-software-market-size-enormous.html https //ictmrfr.blogspot.com/2022/03/ai-in-insurance-industry-enormous.html About Market Research Future At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research Consulting Services. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com
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Market Scenario Market Research Future (MRFR), in its latest report on the global market for blockchain-as-a-service Industry 2020, reveals factors that govern the market. The increase in the adoption of Blockchain-as-a-Service can support the expansion of the market across the analysis period. As per MRFR data, the expansion of the blockchain-as-a-service market is likely to be at 70.63% CAGR across the study period 2018–2024. The blockchain-as-a-service market value can touch USD 18,981.66 Million by 2024. The increase in the application of blockchain-as-a-service solutions for financial service, banking, and insurance enterprises can bolster the expansion of the market across the analysis period. Moreover, the rise in the adoption of e-commerce organizations, retail and government entities can drive the market growth. The rise in the demand to implement BaaS solutions in supply chain management for various industries can also promote the expansion of the market through the study period. BaaS solutions are also being used for international business transactions due to the high reliability and security offered by them. On the contrary, lack of expertise on blockchain technology, less interoperability of blockchain solutions among enterprises, issues in regulatory standards, and issues with high costs associated with the integration of legacy systems can restrain the expansion of the blockchain-as-a-service solutions market in the coming years. Rivers Growing Integration of AI and Blockchain Technologies to Boost Market Growth The growing integration of AI (artificial intelligence) and blockchain technologies with solutions will boost the market over the forecast period. Opportunities Rising Adoption of Cloud-based Services to offer Robust Opportunities The rising adoption of cloud-based services by enterprises will offer robust opportunities for the market in the forecast period. Enterprises are adopting cloud-based services increasingly for their business functions owing to their different benefits like agility, disaster recovery, flexibility, and lower costs. Besides the cloud offers the perks of hybrid cloud deployment that offers the perks of both the public and private clouds. Due to cloud deployment, enterprises can avail the Blockchain As A Service security capabilities without the need for any complex infrastructure. Restraints and Challenges Security Issues and Privacy Concerns to act as Market Restraint The security issues and privacy concerns of Blockchain As A Service Market stored on the cloud may act as a market restraint over the forecast period. Besides, the integration of Blockchain As A Service as a service into the existing systems may also impede market growth. COVID-19 Analysis The BaaS market has been substantially impacted owing to the increasing pandemic situation across the globe. The COVID-19 outbreak has led to reduced business activities as governments had enforced lockdowns. Owing to this, the dependency on online businesses has significantly grown to offer necessary services to consumers. Thus the increased need for Blockchain As A Service as a service as this offers robustness and security to the data. BaaS simplifies business processes and also affording transparency and immutability as well as increase focus on operational efficiency. Also, BaaS devices are also used in retail malls and buildings to screen people during the crisis before they enter. They can detect this using AI technology if anyone is not wearing a mask or has high temperature. Further, touchless technologies are also flooding the market now. Airports, hospitals, offices, and secure locations are making the most of non-contact Blockchain As A Service attendance. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/7942 Competitive Outlook Microsoft Corporation, IBM Corporation, Oracle Corporation, SAP SE, Amazon Web Services, Accenture PLC, Cognizant, Deloitte Touche Tohmatsu Limited, Capgemini SE, Infosys Limited Huawei Technologies Co. Ltd, NTT Data Corporation, Tata Consultancy Services Limited, HPE, Baidu, Inc., Wipro Limited, and KPMG among others are some notable developers of blockchain-as-a-service solutions as listed by MRFR. Segmentation The segment evaluation of the blockchain-as-a-service solutions market is done by component, platform, application, cloud, and organization size. The platform based segments of the blockchain-as-a-service solutions market Ethereum, Ripple, Hyperledger, R3, and others. The component based segments of the blockchain-as-a-service solutions market are tools and services The cloud based segments of the blockchain-as-a-service solutions market are hybrid, private, and public. The organization size based segments of the blockchain-as-a-service solutions market are large enterprises and SMEs. The application based segments of the blockchain-as-a-service solutions market are smart contracts, compliance management, inventory management, identity management, payment management, supply chain management, fraud management, loyalty and rewards management and others. The vertical based segments of the blockchain-as-a-service solutions market are retail and e-commerce, transportation logistics, BFSI, healthcare, IT telecommunications, government, media entertainment, energy utilities, and others. Regional Analysis Trends of the blockchain-as-a-service market are studied across North America, APAC, MEA, and EU. As per MRFR regional data, the blockchain-as-a-service market in North America is known as to have the largest market share. The high rate of adoption of blockchain technology across the US, following Canada and Mexico can support the expansion of the blockchain-as-a-service market in the review period. The well-established BFSI vertical in the region, being the high end-user of blockchain-as-a-service solutions can promote the expansion of the regional market. In EU, the presence of noteworthy marketers and giant tech players such as Microsoft Corporation, IBM Corporation, and Amazon Web Services, can support the expansion of the blockchain-as-a-service market in the region in the years to come. In Asia Pacific, the regional blockchain-as-a-service market is expected to boom due to the gradual rise in the application of blockchain solutions in cash rich BFSI and retail industry. The blockchain-as-a-service market in Asia Pacific is expected to generate the highest revenue for the global market in the years ahead. The rise of the market in MEA is gradual. Access Report Details @ https //www.marketresearchfuture.com/reports/blockchain-service-market-7942 Table of Contents 1Executive Summary 2Scope of the Report 2.1Market Definition 2.2Scope of the Study 2.2.1Research objectives 2.2.2Assumptions Limitations 2.3Markets Structure Continued…. Similar Report Application Management Services Market By Service-Type (System Integration, Consulting Services, Modernization Services, And Others), By Organization Size, By Deployment, And By End-Users Open Source Intelligence (OSINT) Market By Security Type (Human Intelligence, Content Intelligence, Dark Web Analysis, Link/Network Analysis, Data Analytics, Text Analytics, Artificial Intelligence, Big Data, Others), Technology (Bid Data Software, Video Analytics, Text Analytics, Visualization Tool, Cyber Security, Web Analysis, Social Media Analysis, Others), Application (Military Defense, Homeland Security, Private Sector, Public Sector, National Security, Others) About Market Research Future Market Research Future (MRFR) has created a niche in the world of market research. It is counted among the top market research companies that offer well-researched and updated market research reports and insights to businesses of all sizes. What sets us apart is our super-responsive team that offers quality work keeping clients abridged of the prospective challenges and opportunities in various markets. Our team is adept in their space as well as patiently listens to every client. The best part is they know their work inside out and possess the expertise to guide the client in the right direction and achieve results on a tight deadline. We are a one-stop solution for all your data research needs. Our team does not believe in the “one size fits all” approach to creating a report that is detailed and concise. We handle 13 industry verticals including Healthcare, Chemicals and Materials, Information and Communications Technology, Semiconductor and Electronics, Energy and Power, Food, Beverages Nutrition, Automobile, Consumer and Retail, Aerospace and Defense, Industrial Automation and Equipment, Packaging Transport, Construction, and Agriculture. With our unique approach for every market report, we aim to reach the zenith in qualitative business intelligence and syndicated market research. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com #market #research #industry #data #growth #trend #report #analyis #share #marketing #forecast #digital #geographic #demographic #gnews Plugin Error キーワードを入力してください。 #tech #researchreport #marketreport #futrue
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SonicStage 傳輸(這篇文章適用於SO906i/SO905iCS) 既然這支是日本SE出品的手機,理所當然的就可以使用Sonic Stage來傳輸音樂. 首先要先下載SonicStage CP.這時一定會感覺,為什麼不用最新版的SonicStage V呢?原因很簡單,因為不支援這支手機. SonicStageCP下載(網路安裝版) 首先就是要把歌曲匯進SonicStage. 這支手機不支援Sony自有的格式ATRAC3,所以這裡只匯入AAC檔. 再來就是幫歌曲加上專輯封面.對著歌曲按右鍵,選擇內容.(如下圖) 把圖片拉到滑鼠游標的那個地方放開,或是使用"追加(A)"這個按鈕來新增專輯封面(圖片大小一樣是200x200,超過就會縮小,請注意.) 加入圖片後只要按下OK就可以了. 在傳輸進手機之前,可以先新增播放清單.這樣可把播放清單也同步進去. 新增播放清單的方式 先切換成顯示播放清單. 選擇這個功能新增播放清單. 輸入你想要的名稱(注意 有些中文在手機內是不能顯示的) 再來把音樂加入到播放清單內,操作方式如下. 選擇好要加入的音樂按"→"即可新增到播放清單內. 加入後請按一下上方的按鈕就可以結束播放清單編輯模式. 最後就是把音樂傳輸到手機內了,把傳輸線接上手機並把傳輸模式切換成"MicroSD"模式. SonicStage成功的辨認出SO906i的記憶卡. 因為是用播放清單傳輸進去,所以建議切換成顯示播放清單模式會比較好. 在左邊選擇好要傳輸的播放清單,按下"→"就可以把音樂傳輸進去. 傳輸進去後可以把播放清單展開來看看是不是把音樂正常傳輸進去了. 在手機裡也顯示出來,專輯封面都有顯示. 接下來就可以好好的享受音樂了.
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!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "DTD/xhtml1-transitional.dtd" !-- Japanese Translated by Testing Engineer s Forum (TEF) in Japan, Working Group of TestLink Japanese Translation Project -- html xmlns="http //www.w3.org/1999/xhtml" lang="jp" head meta http-equiv="Content-Type" content="text/html; charset=UTF-8" / meta http-equiv="Content-language" content="jp" / meta name="author" content="Martin Havlat" / meta name="copyright" content="GNU" / meta name="robots" content="NOFOLLOW" / title TestLink Help Glossary of Terms /title link rel="stylesheet" type="text/css" href="{$basehref}{$smarty.const.TL_THEME_CSS_DIR}tl_docs.css" / /head body h1 TestLinkで使用されている用語の注解 /h1 div id="menu" a href="javascript window.close();" class="tlButton" 閉じる /a a href="javascript history.back();" class="tlButton" 戻る /a a href="{$basehref}lib/general/show_help.php?help=content locale={$locale}" class="tlButton" ヘルプ 目次 /a /div h2 範囲 /h2 p この資料は設計概念を表示しています、 用語と定義はテストの専門用語を理解することに役立ちます。 イタリック体の説明はTestLinkの語法です。 /p h2 定義 /h2 ul li a name="actualoutcome" /a b 実際の結果 /b テストでコンポーネントやシステムが出力、表示した結果。 /li li a name="bug" /a b バグ /b (= a href="#fault" フォールト /a )要求された機能をコンポーネントまたはシステムに果たせなくする、コンポーネントまたはシステム中の不備。たとえば不正な命令、データ定義。実行中に欠陥に遭遇した場合、コンポーネントまたはシステムの故障を引き起こす。 /li li a name="coverageitem" /a b カバレッジ アイテム /b a href="#testing" テスト /a /li カバレッジの基礎となる実体や属性。たとえば同値分割やコード命令。 li a name="error" /a b エラー /b 間違った結果を生み出す人間の行為。 /li li a name="expectedresults" /a b 予想結果 /b (= 予測結果 または 予想結果)指定の条件下で a href="#testspec" 仕様 /a やほかの情報から予測できるコンポーネントやシステムの動作。 /li li a name="failure" /a b 故障 /b コンポーネントやシステムが期待した機能、サービス、結果を提供できないこと。 /li li a name="fault" /a b フォールト /b (= バグ)要求された機能をコンポーネントまたはシステムに果たせなくする、コンポーネントまたはシステム中の不備。たとえば不正な命令、データ定義。実行中に欠陥に遭遇した場合、コンポーネントまたはシステムの故障を引き起こす。 /li li a name="steps" /a b ステップ /b (= テストシナリオ, 入力)開始点から終了点に至る一連のテスト実行のアクション /li li a name="testproject" /a b テストプロジェクト /b span class="italic" テストプロジェクトはTestLinkデータの一番上位の構造です。 すべてのデータ(ユーザ情報を除いて)は活動しているテストプロジェクトに関連付けられます。 /span /li li a name="testcasesuite" /a b テストケーススイート /b 対象のコンポーネントまたはシステムのために a href="#testcase" テストケース /a をまとめたもの。ひとつのテストの事後条件は次のテストの前提条件として利用される。 span class="italic" TestLinkではテスト仕様またはテスト計画でテストケースをまとめるために使用される。 /span /li li a name="testcoverage" /a b テストカバレッジ /b 指定の網羅条件を a href="#testcasesuite" テストスイート /a が実行した度合。パーセンテージで表す。 /li li b a name="testexecution" /a テスト実行 /b テスト対象のコンポーネントやシステムでテストを実行し a href="#actualoutcome" 実際の結果 /a を出力するプロセス。 br/ span class="italic" テストケーススイートはテストケースから割り当てられたテスト仕様でテストケースを定義している。 /span /li li a name="testplan" /a b テスト計画 /b 計画されたテスト活動の狙い、アプローチ、リソーススケジュールを記述するドキュメント。テストアイテム、テストすべき特色となる機能、テスティングタスク、各タスクを行う人、テスト担当者の独立の度合い、テスト環境、使われるテスト設計技法と入口・出口基準、それらの選択の理論的根拠、それに代替計画を必要とするあらゆるリスクを特定する。これはテスト計画プロセスの記録である。 br/ span class="italic" TestLinkはテスト計画にてユーザの割り当て、ビルド、テストケーススイートを定義できる。 /span /li li a name="testspec" /a b テスト仕様 /b 各々の a href="#testcase" テストケース /a 、 a href="#coverageitem" カバレッジアイテム /a 、テストにおけるソフトウェアの初期状態、 a href="#steps" ステップ /a 、そして a href="#expectedresults" 予想結果 /a 。 /li li a name="testing" /a b テスト /b ひとつ以上のテストケースの組み合わせ /li li a name="testcase" /a b テストケース /b a href="#steps" ステップ /a 、実行前条件値、 a href="#expectedresults" 予想結果 /a そして実行事後条件の組み合わせで、特定のプログラムのシナリオを用いることや指定された要件の遵守を検証することのような特定の目的またはテスト条件のために開発されたもの。 /li /ul /body /html
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LNまとめ http //michaelguth.com/economist/google/bayesian_econometrics_lecture_note.html 授業ページの壁 Course page Hedibert Freitas Lopes Macro dynamics and MCMC A Course in Bayesian Econometrics (University of Queensland, EC6370) 9,330 Introduction to Bayesian Econometrics with applications in Macroeconomics and Finance James D. Hamilton Lecture slides pdfの壁 A course in bayesian econometrics A beginner's notes on... Introduction to Bayesian Intro to Bayesian and Decisition theory Lecture Notes on Bayesian Estimation and Classification 難しそうなpdfの壁 PhD course in Bayesian Nonlinear econometrics MIT
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Nowadays, speed of life that has escalated to mind boggling levels, having a leisure breakfast is a thing of the past, especially in rapidly developing and developed countries. As per comprehensive research done by Future Market Insights in the year 2019, more and more individuals prefer convenient breakfast alternatives that can be eaten on the go or in the workplace. Fast-paced lifestyles and rising women s involvement in the workforce and longer travel times are both driving demand for on-the-go breakfast products around the world, especially in the fast-growing Asia-Pacific region and the developed regions of the European Union and North America. Furthermore, growing urbanization and the notion of nuclear families make working women juggle domestic and work duties and as a result, there is little time to make or have a leisurely breakfast, as the urban population fights increasing commutes and a lack of time. All these factors contribute significantly to the success of quick easy-to-eat and safe on-the-go breakfast products as compared to sugar cereals. Growing demand for prepared goods generates a need in food beverage retail stores to expand the presence of on-the-go breakfast products. For leading F B firms, producing ready-to-eat edibles has become profitable as advanced innovations continue to deliver cost-effectiveness in processes of processing, preservation and packaging. The demand for on-the-go breakfast items is rising at an unparalleled rate globally, suggesting that customers from around the world prefer fast-made but balanced breakfasts such as oatmeal or yoghurt cereals. The biggest problem, however is that there have been many shortcomings in global distribution for on-the-go breakfast products that shorten the supply chain and limit the presence of such products in consumer retail outlets. However, the delivery of on-the-go breakfast items products is projected to be inconsistent in several regions, which is hampering the global market s overall growth. Compared to developing areas, customers in developed countries are more knowledgeable about on-the-go breakfast items. Manufacturers are known to opt for limited distribution networks such as social outlets and e-tailing sites in developing countries such as Brazil, China and India, amongst others. As a result due to less prevalence in other distribution networks, the distribution of on-the-go breakfast items in these regions is weakened. In addition, in contrast to other ready-to-eat products, pricey on-the-go breakfast products often sidetrack customer preferences with respect to packaged breakfast products. Source- https //www.futuremarketinsights.com/reports/on-the-go-breakfast-products-market [[https //www.futuremarketinsights.com/ ]]
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お役立ち情報 スケジュール https //confsearch.ethz.ch/?query=STOC+FOCS+SODA+CCC+ICALP+ITCS+LICS+IPCO+ISSAC+SoCG+PODS+COLT+EC+ESA+STACS+APPROX+RANDOM+MFCS+SWAT+WADS+ISAAC+FUN http //www.conferencelist.info/upcoming.html http //community.dur.ac.uk/tom.friedetzky/conf.html http //www.lix.polytechnique.fr/~hermann/conf.html http //csconf.net/deadlines 国際会議・雑誌 MSAR field ratings (2014) http //www.conferenceranks.com/visualization/msar2014.html Google scholar Top publications https //scholar.google.com/citations?view_op=top_venues vq=eng_theoreticalcomputerscience Ranking of CS Departments based on the Number of Papers in Theoretical Computer Science https //projects.csail.mit.edu/dnd/ranking/ Computer Science Conference Rankings https //webdocs.cs.ualberta.ca/~zaiane/htmldocs/ConfRanking.html Acceptance ratio of some Theoretical Computer Science Conferences https //www.lamsade.dauphine.fr/~sikora/ratio/confs.php ML-DM-AI Papers by Researchers in Japan https //knuu.github.io/pages/ml-dm-ai_jp_papers.html Conference Ranks http //www.conferenceranks.com/ Acceptance rates for the top-tier AI-related conferences https //github.com/lixin4ever/Conference-Acceptance-Rate https //perso.crans.org/genest/conf.html https //www.aminer.org/ranks/conf Computer Science Conference Rankings https //dsl.cds.iisc.ac.in/publications/CS_ConfRank.htm Journals (etc.) in Discrete Mathematics and related fields http //www.math.iit.edu/~kaul/Journals.html List of TCS conferences and workshops https //cstheory.stackexchange.com/questions/7900/list-of-tcs-conferences-and-workshops GII-GRIN-SCIE (GGS) Conference Rating http //www.consorzio-cini.it/gii-grin-scie-rating.html CORE Computer Science Journal Rankings http //cic.tju.edu.cn/faculty/zhileiliu/doc/COREComputerScienceJournalRankings.html Computer Science Conference Rank https //www.camille-kurtz.com/index_fichiers/html/CSRank.html CORE Conference Portal http //portal.core.edu.au/conf-ranks/ CORE Journal Portal http //portal.core.edu.au/jnl-ranks/ 中国计算机学会推荐 https //www.ccf.org.cn/Academic_Evaluation/By_category/ 清华大学计算机学科群 推荐学术会议和期刊列表 https //numbda.cs.tsinghua.edu.cn/~yuwj/TH-CPL.pdf 清华大学交叉信息研究院 重要国际学术会议及核心期刊 https //iiis.tsinghua.edu.cn/uploadfile/cs_conference_list.pdf 頂尖國際會議表列 https //www.csie.ncu.edu.tw/file/98ef5b203937077d24098c335abcf0ca 计算机学术期刊排名 https //sites.google.com/site/luzhaoshomepage/Home/journal-list/ji-suan-ji-xue-shu-qi-kan-pai-ming-computer-science-journal-rankings まとめサイト Best Paper Awards in Computer Science (since 1996) http //jeffhuang.com/best_paper_awards.html データベース勉強会Wiki http //www.kde.cs.tsukuba.ac.jp/dbreading/ Statistics of acceptance rate for the main AI conferences https //github.com/lixin4ever/Conference-Acceptance-Rate Hot Topics on Big Data Algorithms, Analytics and Applications https //www.cse.ust.hk/~leichen/courses/comp6311D/ http //akoide.hatenablog.com/ http //www.orgnet.com/hijackers.html http //11011110.livejournal.com/260838.html http //www.ipsj.or.jp/journal/info/75NC.html 専門知識の仕入れ方 by 吉田さん http //research.preferred.jp/2011/09/how-to-learn/ 岩間研の輪講 http //www.lab2.kuis.kyoto-u.ac.jp/fswikiout/wiki.cgi?action=LIST Laplacian Linear Equations, Graph Sparsification, Local Clustering, Low-Stretch Trees, etc. https //sites.google.com/a/yale.edu/laplacian/ Combinatorial Reconfiguration Wiki http //reconf.wikidot.com/ Connected Papers https //www.connectedpapers.com/ 英語論文の査読表現集 https //staff.aist.go.jp/a.ohta/japanese/study/Review_ex_top.htm Computational Intractability A Guide to Algorithmic Lower Bounds https //hardness.mit.edu/ What Books Should Everyone Read? https //cstheory.stackexchange.com/questions/3253/what-books-should-everyone-read Mathematical Writing by. Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts https //jmlr.csail.mit.edu/reviewing-papers/knuth_mathematical_writing.pdf 講義 PCP and hardness of approximation 解説とか On Dinur s Proof of the PCP Theorem https //www.ams.org/journals/bull/2007-44-01/S0273-0979-06-01143-8/S0273-0979-06-01143-8.pdf クラスNPの新しい特徴づけ https //ipsj.ixsq.nii.ac.jp/ej/index.php?action=pages_view_main active_action=repository_action_common_download item_id=4159 item_no=1 attribute_id=1 file_no=1 page_id=13 block_id=8 https //cstheory.stackexchange.com/questions/45/what-are-good-references-to-understanding-the-proof-of-the-pcp-theorem https //www.cs.umd.edu/~gasarch/TOPICS/pcp/pcp.html https //sites.google.com/view/pcpfest/program Approximability of Optimization Problems (1999?, Madhu Sudan) http //people.csail.mit.edu/madhu/FT99/course.html ( low-degree test ) 😋CSE 532 Computational Complexity Essentials (2004, Paul Beame) https //courses.cs.washington.edu/courses/cse532/04sp/ ( low-degree test ) 😋CSE 533 The PCP Theorem and Hardness of Approximation (2005, Venkatesan Guruswami Ryan O Donnell) https //courses.cs.washington.edu/courses/cse533/05au/ (Dinur s proof) CS 294 PCP and Hardness of Approximation (2006, Luca Trevisan) https //cs.stanford.edu/people/trevisan/pcp/ (講義録少) 😋Course 236603 Probabilistically Checkable Proofs (2007, Eli Ben-Sasson) https //eli.net.technion.ac.il/files/2013/03/notes_2007_Fall.pdf (PCPP; robust PCP) CS359 Hardness of Approximation (Tim Roughgarden, 2007) https //timroughgarden.org/w07b/w07b.html (講義録少) 😋15-854(B) Advanced Approximation Algorithms (2008, Anupam Gupta Ryan O Donnell) https //www.cs.cmu.edu/~anupamg/adv-approx/ 😋6.895 Probabilistically Checkable Proofs and Hardness of Approximation (2010, Dana Moshkovitz) https //www.cs.utexas.edu/~danama/courses/pcp-mit/index.html ( low-degree test ) Prahladh Harsha CMSC 39600 PCPs, codes and inapproximability (2007, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/07autumn/ (講義録少) 😋Limits of approximation algorithms PCPs and Unique Games (2009―10, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/2009-10/limits/ ( low-degree test ) PCPs and Limits of approximation algorithms (2014―15, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/2014-15/limits/ (講義録少) Approximation Algorithms and Hardness of Approximation (2013, Ola Svensson Alantha Newman) https //theory.epfl.ch/osven/courses/Approx13/ (Dinur s proof) 😋CS294 Probabilistically Checkable and Interactive Proof Systems (2019, Alessandro Chiesa) http //people.eecs.berkeley.edu/~alexch/classes/CS294-S2019.html ( 講義動画神 , low-degree test) 15-859T A Theorist s Toolkit (2013, Ryan O Donnell) http //www.cs.cmu.edu/~odonnell/toolkit13/ Algorithmic Lower Bounds Fun with Hardness Proofs (2014/2019, Erik Demaine) https //ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014/index.htm http //courses.csail.mit.edu/6.892/spring19/lectures/ CS395T Pseudorandomness (2017, David Zuckerman) https //www.cs.utexas.edu/~diz/395T/17/ Pseudorandomness (Salil Vadhan, monograph) https //people.seas.harvard.edu/~salil/pseudorandomness/ Expander graphs Expander Graphs and their applications (2020, Irit Dinur) https //www.wisdom.weizmann.ac.il/~dinuri/courses/20-expanders/index.htm Expander Graphs in Computer Science (2010, He Sun) https //resources.mpi-inf.mpg.de/departments/d1/teaching/ws10/EG/WS10.html Course 67659 Expander graphs and their applications (2002, Nati Linial Avi Wigderson) https //www.boazbarak.org/expandercourse/ Counting and Sampling Markov Chain Monte Carlo Methods (2006, Eric Vigoda) https //www.cc.gatech.edu/~vigoda/MCMC_Course/ CSE 599 Counting and Sampling (2017, Shayan Oveis Gharan) https //homes.cs.washington.edu/~shayan/courses/sampling/ CS 294 Markov Chain Monte Carlo Foundations Applications, (Alistair Sinclair) https //people.eecs.berkeley.edu/~sinclair/cs294/f09.html CS294-180 Partition Functions Algorithms Complexity (2020, Alistair Sinclair) https //people.eecs.berkeley.edu/~sinclair/cs294/f20.html CSE 599 Polynomial Paradigm in Algorithm Design (2020, Shayan Oveis Gharan) https //homes.cs.washington.edu/~shayan/courses/polynomials/ Math 270 The Geometry of Polynomials in Algorithms, Combinatorics, and Probability (2015, Nikhil Srivastava) https //math.berkeley.edu/~nikhil/courses/270/ Bridging Continuous and Discrete Optimization (2017) https //simons.berkeley.edu/programs/optimization2017 Geometry of Polynomials https //simons.berkeley.edu/programs/geometry2019 Counting and Sampling (2020, EPFL) https //www.epfl.ch/schools/ic/tcs/counting-and-sampling-2020/ Markov Chains and Counting (Alan Frieze, book) https //www.math.cmu.edu/~af1p/Teaching/MCC17/MC.html Others Parameterized Complexity (2019, Saket Saurabh) https //sites.google.com/view/sakethome/teaching/parameterized-complexity Proofs, beliefs, and algorithms through the lens of sum-of-squares https //www.sumofsquares.org/public/index.html Stat260/CompSci294 Topics in Spectral Graph Methods (Michael Mahoney) https //www.stat.berkeley.edu/~mmahoney/s15-stat260-cs294/ Topics in Theoretical Computer Science An Algorithmist s Toolkit (Jonathan Kelner) https //ocw.mit.edu/courses/mathematics/18-409-topics-in-theoretical-computer-science-an-algorithmists-toolkit-fall-2009/ 6.889 Algorithms for Planar Graphs and Beyond (Fall 2011) http //courses.csail.mit.edu/6.889/fall11/lectures/ 15-855 Graduate Computational Complexity Theory (2017, Ryan O Donnell) http //www.cs.cmu.edu/~odonnell/complexity17/ その他 Journals with quick reviewing - Theoretical Computer Science Stack Exchange https //cstheory.stackexchange.com/questions/8335/journals-with-quick-reviewing Backlog of MathematicsResearch Journals https //www.ams.org/journals/notices/201810/rnoti-p1289.pdf Online TCS Seminars https //cstheory.stackexchange.com/questions/46930/online-tcs-seminars Algorithms Randomization Computation https //sites.google.com/di.uniroma1.it/arc/home Felix Reidl https //tcs.rwth-aachen.de/~reidl/ https //rjlipton.wordpress.com/2014/12/21/modulating-the-permanent/ https //barthesi.gricad-pages.univ-grenoble-alpes.fr/personal-website/dpps/2018-26-11-dpps_intro/ Thirty-Three Miniatures Mathematical and Algorithmic Applications of Linear Algebra https //kam.mff.cuni.cz/~matousek/stml-53-matousek-1.pdf Research in Progress https //researchinprogress.tumblr.com/ 情報拡散 投票者モデル A model for spatial conflict Biometrika 1973 Ergodic theorems for weakly interacting infinite systems and the voter model Annals of Probability 1975. Influence Maximization 関連 バイラルマーケティング Mining the Network Value of Customers Mining Knowledge-Sharing Sites for Viral Marketing 元ネタ Maximizing the Spread of Influence through a Social Network 理論的結果 On the Approximability of Influence in Social Networks 影響最大化／影響力推定の爆速アルゴリズム シミュレーション CELF++ Optimizing the Greedy Algorithm for Influence Maximization in Social ... WWW 2011 Efficient Influence Maximization in Social Networks KDD 2009 StaticGreedy Solving the Scalability-Accuracy Dilemma in Influence Maximization CIKM 2013 UBLF An Upper Bound Based Approach to Discover Influential Nodes in Social ... ICDM 2013 An Upper Bound based Greedy Algorithm for Mining Top-k Influential Nodes in ... WWW 2014 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2007 IMGPU GPU-Accelerated Influence Maximization in Large-Scale Social Networks TPDS 2014 Influence Maximization in Big Networks An Incremental Algorithm for ... IJCAI 2015 Influence at Scale Distributed Computation of Complex Contagion in Networks KDD 2015 Outward Influence and Cascade Size Estimation in Billion-scale Networks SIGMETRICS 2017 RIS Maximizing Social Influence in Nearly Optimal Time SODA 2014 Influence Maximization Near-Optimal Time Complexity Meets Practical Efficiency SIGMOD 2014 Social Influence Spectrum with Guarantees Computing More in Less Time CSoNet 2015 Influence Maximization in Near-Linear Time A Martingale Approach SIGMOD 2015 Cost-aware Targeted Viral Marketing in Billion-scale Networks INFOCOM 2016 Stop-and-Stare Optimal Sampling Algorithms for Viral Marketing in ... SIGMOD 2016 Revisiting the Stop-and-Stare Algorithms for Influence Maximization PVLDB 2017 Why approximate when you can get the exact? Optimal Targeted Viral Marketing ... INFOCOM 2017 Importance Sketching of Influence Dynamics in Billion-scale Networks ICDM 2017 ヒューリスティクス Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale ... KDD 2010 Scalable Influence Maximization in Social Networks under the Linear ... ICDM 2010 IRIE Scalable and Robust Influence Maximization in Social Networks ICDM 2012 Simulated Annealing Based Influence Maximization in Social Networks AAAI 2011 On Approximation of Real-World Influence Spread PKDD 2012 Scalable and Parallelizable Processing of Influence Maximization for ... ICDE 2013 Simpath An Efficient Algorithm for Influence Maximization under the Linear ... ICDM 2011 Probabilistic Solutions of Influence Propagation on Networks CIKM 2013 Community-based Greedy Algorithm for Mining Top-K Influential Nodes in ... KDD 2010 Efficient algorithms for influence maximization in social networks KAIS 2012 CINEMA Conformity-Aware Greedy Algorithm for Influence Maximization in ... EDBT 2013 A Novel and Model Independent Approach for Efficient Influence Maximization ... WISE 2013 Influence Spread in Large-Scale Social Networks - A Belief Propagation Approach ECML PKDD 2012 IMRank Influence Maximization via Finding Self-Consistent Ranking SIGIR 2014 ASIM A Scalable Algorithm for Influence Maximization under the Independent ... WWW 2015 Holistic Influence Maximization Combining Scalability and Efficiency with ... SIGMOD 2016 影響拡散高速計算 Efficient influence spread estimation for influence maximization under the ... Exact Computation of Influence Spread by Binary Decision Diagrams WWW 2017 Computing and maximizing influence in linear threshold and triggering models NIPS 2016 その他 Influence Maximization in Undirected Networks SODA 2014 Debunking the Myths of Influence Maximization An In-Depth Benchmarking Study SIGMOD 2017 謎 Maximizing the Spread of Cascades Using Network Design UAI 2010 The complexity of influence maximization problem in the deterministic linear ... JCO 2012 目的関数が違う Personalized Influence Maximization on Social Networks Stability of Influence Maximization Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a ... On minimizing budget and time in influence propagation over social networks Minimizing Seed Set for Viral Marketing Online Influence Maximization Minimum-Cost Information Dissemination in Social Networks Robust Influence Maximization (He-Kempe) Robust Influence Maximization (Chen+) Robust Influence Maximization (Lowalekar+) Spheres of Influence for More Effective Viral Marketing 変種設定 インターネット広告 Real-time Targeted Influence Maximization for Online Advertisements VLDB 2015 Viral Marketing Meets Social Advertising Ad Allocation with Minimum Regret VLDB 2015 Revenue Maximization in Incentivized Social Advertising VLDB 2017 疎化・粗大化 Sparsification of Influence Networks Fast Influence-based Coarsening for Large Networks 予測 Prediction of Information Diffusion Probabilities for Independent Cascade Model Learning Continuous-Time Information Diffusion Model for Social Behavioral ... Learning Influence Probabilities In Social Networks Learning Stochastic Models of Information Flow Predicting Information Diffusion on Social Networks with Partial Knowledge Latent Feature Independent Cascade Model for Social Propagation Learning Diffusion Probability based on Node Attributes in Social Networks Topic-aware Social Influence Propagation Models Uncovering the Temporal Dynamics of Diffusion Networks モデリング 時間 A Data-Based Approach to Social Influence Maximization Time-Critical Influence Maximization in Social Networks with Time-Delayed ... Time Constrained Influence Maximization in Social Networks Uncovering the Temporal Dynamics of Diffusion Networks On Influential Node Discovery in Dynamic Social Networks Influence Maximization with Novelty Decay in Social Networks トピック・カテゴリ Topic-aware Social Influence Propagation Models Diversified Social Influence Maximization モデルは同じ，目的関数が違う トピック・カテゴリのアルゴリズム Online Topic-aware Influence Maximization Queries EDBT 2014 Real-time Topic-aware Influence Maximization Using Preprocessing CSoNet 2015 Online Topic-Aware Influence Maximization VLDB 2015 負/競合 Competitive Influence Maximization in Social Networks WINE 2007 Word of Mouth Rumor Dissemination in Social Networks SIROCCO 2008 Threshold Models for Competitive Influence in Social Networks WINE 2010 Influence Maximization in Social Networks When Negative Opinions May Emerge ... Influence Blocking Maximization in Social Networks under the Competitive ... Maximizing Influence in a Competitive Social Network A Follower s Perspective ICEC 2007 New Models for Competitive Contagion Opinion maximization in social networks 意見 Maximizing Influence in an Ising Network A Mean-Field Optimal Solution Isingモデル 投票者モデル オリジナル Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model A Model for Spatial Conflict A Note on Maximizing the Spread of Influence in Social Networks WINE 2007 Influence Diffusion Dynamics and Influence Maximization in Social Networks ... WSDM 2013 Maximizing the Long-term Integral Influence in Social Networks Under the ... WWW 2014 適応的二段階アプローチ Scalable Methods for Adaptively Seeding a Social Network WWW 2015 その他 How to Influence People with Partial Incentives Mining Social Networks Using Heat Diffusion Processes for Marketing ... Influence Maximization with Viral Product Design Profit Maximization over Social Networks On Budgeted Influence Maximization in Social Networks In Search of Influential Event Organizers in Online Social Networks Linear Computation for Independent Social Influence Efficient Location-Aware Influence Maximization Dynamic Influence Maximization Under Increasing Returns to Scale Online Influence Maximization Real-time Targeted Influence Maximization for Online Advertisements VLDB 2015 連続時間独立カスケード(CT-IC)モデル Uncovering the Temporal Dynamics of Diffusion Networks ICML 2011 Influence Maximization in Continuous Time Diffusion Networks ICML 2012 Scalable Influence Estimation in Continuous-Time Diffusion Networks NIPS 2013 Tight Bounds for Influence in Diffusion Networks and Application to Bond ... NIPS 2014 Anytime Influence Bounds and the Explosive Behavior of Continuous-Time ... NIPS 2015 汚染最小化 Minimizing the Spread of Contamination by Blocking Links in a Network Blocking Links to Minimize Contamination Spread in a Social Network Negative Influence Minimizing by Blocking Nodes in Social Networks Finding Spread Blockers in Dynamic Networks 動的アルゴリズム Influence Maximization in Dynamic Social Networks Maximizing the Extent of Spread in a Dynamic Network On Influential Nodes Tracking in Dynamic Social Networks Real-Time Influence Maximization on Dynamic Social Streams PVLDB 2017 斉藤 和巳さん一派 Tractable Models for Information Diffusion in Social Networks PKDD 2006 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2007 Minimizing the Spread of Contamination by Blocking Links in a Network AAAI 2008 Prediction of Information Diffusion Probabilities for Independent Cascade Model KES 2008 Learning Continuous-Time Information Diffusion Model for Social Behavioral ... ACML 2009 Selecting Information Diffusion Models over Social Networks for Behavioral ... ECML PKDD 2010 (ACML 09と同じ？) Blocking Links to Minimize Contamination Spread in a Social Network TKDD 2009 Finding Influential Nodes in a Social Network from Information Diffusion Data SBP 2009 Learning information diffusion model in a social network for predicting influence of nodes Intell. Data Anal. 2011 Learning Diffusion Probability based on Node Attributes in Social Networks ISMIS 2011 Uncertain Graphs On a Routing Problem Within Probabilistic Graphs ... INFOCOM 2007 The Most Reliable Subgraph Problem PKDD 2007 Frequent Subgraph Pattern Mining on Uncertain Graph Data CIKM 2009 Fast Discovery of Reliable Subnetworks ASONAM 2010 k-Nearest Neighbors in Uncertain Graphs VLDB 2010 Finding Top-k Maximal Cliques in an Uncertain Graph ICDE 2010 Fast Discovery of Reliable k-terminal Subgraphs PAKDD 2010 Discovering Frequent Subgraphs over Uncertain Graph Databases under ... KDD 2010 BMC An Efficient Method to Evaluate Probabilistic Reachability Queries DASFAA 2011 Efficient Discovery of Frequent Subgraph Patterns in Uncertain Graph Databases EDBT 2011 Discovering Highly Reliable Subgraphs in Uncertain Graphs KDD 2011 Distance Constraint Reachability Computation in Uncertain Graphs VLDB 2011 Efficient Subgraph Search over Large Uncertain Graphs VLDB 2011 Reliable Clustering on Uncertain Graphs ICDM 2012 Polynomial-Time Algorithm for Finding Densest Subgraphs in Uncertain Graphs MLG 2013 Clustering Large Probabilistic Graphs TKDE 2013 The Pursuit of a Good Possible World Extracting Representative Instances of ... SIGMOD 2014 Efficient and Accurate Query Evaluation on Uncertain Graphs via Recursive ... ICDE 2014 Fast Reliability Search in Uncertain Graphs EDBT 2014 Top-k Reliable Edge Colors in Uncertain Graphs CIKM 2015 Top-k Reliability Search on Uncertain Graphs ICDM 2015 Assessing Attack Vulnerability in Networks with Uncertainty INFOCOM 2015 Triangle-Based Representative Possible Worlds of Uncertain Graphs DASFAA 2016 Truss Decomposition of Probabilistic Graphs Semantics and Algorithms SIGMOD 2016 ネットワーク信頼性 A practical bounding algorithm for computing two-terminal reliability based ... Comput. Math. Appl. 2011 OR系 Minimum-Risk Maximum Clique Problem k-means Streaming k-means approximation StreamKM++ A Clustering Algorithm for Data Streams k-means++ The Advantages of Careful Seeding Streaming k-means on Well-Clusterable Data A Local Search Approximation Algorithm for k-Means Clustering Fast and Accurate k-means For Large Datasets Hartigan s Method k-means Clustering without Voronoi Hartigan s K-Means Versus Lloyd s K-Means - Is It Time for a Change? Using the Triangle Inequality to Accelerate k-Means Making k-means even faster Accelerated k-means with adaptive distance bounds PageRank 高速計算 Extrapolation Methods for Accelerating PageRank Computations FAST-PPR Scaling Personalized PageRank Estimation for Large Graphs 動的更新 Link Evolution Analysis and Algorithms Fast Incremental and Personalized PageRank PageRank on an Evolving Graph Efficient PageRank Tracking in Evolving Networks 私，前原貴憲，河原林健一 バックボタン The Effect of the Back Button in a Random Walk Application for PageRank BackRank an Alternative for PageRank? Spectral Clustering A Random Walks View of Spectral Segmentation Kernel k-means, Spectral Clustering and Normalized Cuts http //ranger.uta.edu/~chqding/Spectral/ https //arxiv.org/abs/0711.0189 A Tutorial on Spectral Clustering. Ulrike von Luxburg Laplacian https //sites.google.com/a/yale.edu/laplacian/ 理論計算機科学 + ... ACM Symposium on Theory of Computing STOC 2013 Fast Approximation Algorithms for the Diameter and Radius of Sparse Graphs STOC 2014 The matching polytope has exponential extension complexity Approximation Algorithms for Regret-Bounded Vehicle Routing and Applications ... Approximate Distance Oracle with Constant Query Time Zig-zag Sort A Simple Deterministic Data-Oblivious Sorting Algorithm ... Minimum Bisection is Fixed Parameter Tractable IEEE Symposium on Foundations of Computer Science FOCS 2013 https //sites.google.com/site/tcsreading/home/focs2013 The Price of Stability for Undirected Broadcast Network Design with Fair ... Learning Sums of Independent Integer Random Variables OSNAP Faster numerical linear algebra algorithms via sparser subspace ... Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for ... Algebraic Algorithms for b-Matching, Shortest Undirected Paths, and f-Factors Improved approximation for 3-dimensional matching via bounded pathwidth ... Independent Set, Induced Matching, and Pricing Connections and Tight ... Approximating Minimum-Cost k-Node Connected Subgraphs via Independence-Free ... Online Node-weighted Steiner Forest and Extensions via Disk Paintings An LMP O(log n)-Approximation Algorithm for Node Weighted Prize Collecting ... Approximating Bin Packing within O(log OPT*loglog OPT) bins Strong Backdoors to Bounded Treewidth SAT ACM-SIAM Symposium on Discrete Algorithms SODA 2008 On the Approximability of Influence in Social Networks SODA 2014 Maximizing Social Influence in Nearly Optimal Time Influence Maximization in Undirected Networks International Symposium on Algorithms and Computation ACM Conference on Innovations in Theoretical Computer Science アルゴリズム + ... Workshop on Algorithm Engineering and Experiments ALENEX 2016 Computing Top-k Closeness Centrality Faster in Unweighted Graphs International Symposium on Experimental Algorithms SEA 2015 Is Nearly-linear the Same in Theory and Practice? A Case Study with a ... Workshop on Algorithms and Models for the Web Graph WAW 2012 Dynamic PageRank using Evolving Teleportation SIGMETRICS 2017 Outward Influence and Cascade Size Estimation in Billion-scale Networks ジャーナル版はProceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) データマイニング + ... ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD 2001 Mining the Network Value of Customers Co-clustering documents and words using Bipartite Spectral Graph Partitioning KDD 2002 Mining Knowledge-Sharing Sites for Viral Marketing KDD 2007 ✔Cost-effective Outbreak Detection in Networks KDD 2008 ✔Influence and Correlation in Social Networks KDD 2009 Efficient Influence Maximization in Social Networks ✔On Compressing Social Networks KDD 2010 Inferring Networks of Diffusion and Influence Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale ... Community-based Greedy Algorithm for Mining Top-K Influential Nodes in ... Discovering Frequent Subgraphs over Uncertain Graph Databases under ... Semi-Supervised Feature Selection for Graph Classification KDD 2011 Discovering Highly Reliable Subgraphs in Uncertain Graphs Sparsification of Influence Networks KDD 2012 Streaming Graph Partitioning for Large Distributed Graphs PageRank on an Evolving Graph Information Diffusion and External Influence in Networks Vertex Neighborhoods, Low Conductance Cuts, and Good Seeds for Local ... Information Propagation Game a Tool to Acquire Human Playing Data for ... Chromatic Correlation Clustering Efficient Personalized PageRank with Accuracy Assurance KDD 2013 Denser than the Densest Subgraph Extracting Optimal Quasi-Cliques with ... Redundancy-Aware Maximal Cliques Trial and Error in Influential Social Networks Workshop on Mining and Learning with Graphs (MLG) Polynomial-Time Algorithm for Finding Densest Subgraphs in Uncertain Graphs KDD 2014 Stability of Influence Maximization Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a ... Heat Kernel Based Community Detection Balanced Graph Edge Partition Correlation Clustering in MapReduce Streaming Submodular Maximization Massive Data Summarization on the Fly Fast Influence-based Coarsening for Large Networks FAST-PPR Scaling Personalized PageRank Estimation for Large Graphs KDD 2015 Influence at Scale Distributed Computation of Complex Contagion in Networks Efficient Algorithms for Public-Private Social Networks Reciprocity in Social Networks with Capacity Constraints Online Influence Maximization Locally Densest Subgraph Discovery ✔Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling Non-exhaustive, Overlapping Clustering via Low-Rank Semidefinite Programming KDD 2016 ✔Robust Influence Maximization (He-Kempe) Robust Influence Maximization (Chen+) FRAUDAR Bounding Graph Fraud in the Face of Camouflage KDD 2018 Approximating the Spectrum of a Graph IEEE International Conference on Data Mining ICDM 2006 Fast Random Walk with Restart and Its Applications ICDM 2010 Scalable Influence Maximization in Social Networks under the Linear ... Modeling Information Diffusion in Implicit Networks ICDM 2011 Simpath An Efficient Algorithm for Influence Maximization under the Linear ... On the Hardness of Graph Anonymization Overlapping correlation clustering Minimizing Seed Set for Viral Marketing ICDM 2012 Reliable Clustering on Uncertain Graphs IRIE Scalable and Robust Influence Maximization in Social Networks Predicting Directed Links using Nondiagonal Matrix Decompositions Inferring the Underlying Structure of Information Cascades Topic-aware Social Influence Propagation Models Time Constrained Influence Maximization in Social Networks Profit Maximization over Social Networks ICDM 2013 Influence Maximization in Dynamic Social Networks UBLF An Upper Bound Based Approach to Discover Influential Nodes in Social ... Influence-based Network-oblivious Community Detection Linear Computation for Independent Social Influence ICDM 2014 Quick Detection of High-degree Entities in Large Directed Networks ICDM 2015 Top-k Reliability Search on Uncertain Graphs ✔Catching the head, tail, and everything in between a streaming algorithm ... ICDM 2017 Importance Sketching of Influence Dynamics in Billion-scale Networks European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases PKDD 2006 Tractable Models for Information Diffusion in Social Networks PKDD 2007 The Most Reliable Subgraph Problem PKDD 2012 On Approximation of Real-World Influence Spread ECML PKDD 2010 Selecting Information Diffusion Models over Social Networks for Behavioral ... ECML PKDD 2012 Influence Spread in Large-Scale Social Networks - A Belief Propagation Approach ECML PKDD 2016 Temporal PageRank SIAM International Conference on Data Mining SDM 2010 Fast Single-Pair SimRank Computation SDM 2011 Influence Maximization in Social Networks When Negative Opinions May Emerge ... Maximising the Quality of Influence SDM 2012 On Influential Node Discovery in Dynamic Social Networks Influence Blocking Maximization in Social Networks under the Competitive ... ✔Fast Robustness Estimation in Large Social Graphs Communities and Anomaly ... SDM 2013 Triadic Measures on Graphs The Power of Wedge Sampling k-means-- A unified approach to clustering and outlier detection Opinion maximization in social networks SDM 2014 Influence Maximization with Viral Product Design Future Influence Ranking of Scientific Literature VoG Summarizing and Understanding Large Graphs Make It or Break It Manipulating Robustness in Large Networks Accelerating Graph Adjacency Matrix Multiplications with Adjacency Forest SDM 2015 Selecting Shortcuts for a Smaller World Where Graph Topology Matters The Robust Subgraph Problem On Influential Nodes Tracking in Dynamic Social Networks ✔Non-exhaustive, Overlapping k-means SDM 2017 A Dual-tree Algorithm for Fast k-means Clustering with Large k Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD 2010 Fast Discovery of Reliable k-terminal Subgraphs ソーシャルネットワーク + ... IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2009 Spectral Counting of Triangles in Power-Law Networks via Element-Wise ... Reducing Social Network Dimensions Using Matrix Factorization Methods Dynamic and Static Influence Models on Starbucks Networks ASONAM 2010 Fast Discovery of Reliable Subnetworks ASONAM 2011 Dynamic Social Influence Analysis through Time-dependent Factor Graphs ASONAM 2012 Influence of the Dynamic Social Network Timeframe Type and Size on the Group ... Diffusion Centrality in Social Networks Visual Analysis of Dynamic Networks using Change Centrality ASONAM 2014 Diversified Social Influence Maximization ASONAM 2015 Structure-Preserving Sparsification of Social Networks ACM Conference on Online Social Networks COSN 2013 Scalable Similarity Estimation in Social Networks Closeness, Node Labels, ... Counting Triangles in Large Graphs using Randomized Matrix Trace Estimation International Conference on Computational Social Networks CSoNet 2015 Real-time Topic-aware Influence Maximization Using Preprocessing Social Influence Spectrum with Guarantees Computing More in Less Time SNA-KDD (International Workshop on Social Network Mining and Analysis) Finding Spread Blockers in Dynamic Networks データベース + ... ACM SIGMOD International Conference on Management of Data SIGMOD 2011 On k-skip Shortest Paths Local Graph Sparsification for Scalable Clustering SIGMOD 2013 Massive Graph Triangulation Efficiently Computing k-Edge Connected Components via Graph Decomposition I/O Efficient Computing SCCs in Massive Graphs TurboISO Towards Ultrafast and Robust Subgraph Isomorphism Search in Large ... TF-Label a Topological-Folding Labeling Scheme for Reachability Querying in ... Online Search of Overlapping Communities Efficient Ad-hoc Search for Personalized PageRank SIGMOD 2014 In Search of Influential Event Organizers in Online Social Networks Efficient Location-Aware Influence Maximization Querying K-Truss Community in Large and Dynamic Graph The Pursuit of a Good Possible World Extracting Representative Instances of ... Influence Maximization Near-Optimal Time Complexity Meets Practical Efficiency SIGMOD 2015 COMMIT A Scalable Approach to Mining Communication Motifs from Dynamic Networks Minimum Spanning Trees in Temporal Graphs Influence Maximization in Near-Linear Time A Martingale Approach SIGMOD 2016 Spheres of Influence for More Effective Viral Marketing ✔Speedup Graph Processing by Graph Ordering Distributed Set Reachability ✔Truss Decomposition of Probabilistic Graphs Semantics and Algorithms Holistic Influence Maximization Combining Scalability and Efficiency with ... Stop-and-Stare Optimal Sampling Algorithms for Viral Marketing in ... TIM+やIMMより高性能（と謳う）影響最大化アルゴリズム SIGMOD 2017 Debunking the Myths of Influence Maximization An In-Depth Benchmarking Study Computing A Near-Maximum Independent Set in Linear Time by Reducing-Peeling DAG Reduction Fast Answering Reachability Queries Scaling Locally Linear Embedding Dynamic Density Based Clustering IEEE International Conference on Data Engineering ICDE 2010 Finding Top-k Maximal Cliques in an Uncertain Graph ICDE 2011 Outlier Detection in Graph Streams ICDE 2012 Learning Stochastic Models of Information Flow Extracting Analyzing and Visualizing Triangle K-Core Motifs within Networks ICDE 2013 Scalable and Parallelizable Processing of Influence Maximization for ... Scalable Maximum Clique Computation Using MapReduce Faster Random Walks By Rewiring Online Social Networks On-The-Fly Sampling Node Pairs Over Large Graphs ICDE 2014 How to Partition a Billion-Node Graph Random-walk Domination in Large Graphs Evaluating Multi-Way Joins over Discounted Hitting Time Efficient and Accurate Query Evaluation on Uncertain Graphs via Recursive ... International Conference on Very Large Data Bases VLDB 2010 Shortest Path Computation on Air Indexes Fast Incremental and Personalized PageRank k-Nearest Neighbors in Uncertain Graphs VLDB 2011 On Triangulation-based Dense Neighborhood Graph Discovery Distance Constraint Reachability Computation in Uncertain Graphs Efficient Subgraph Search over Large Uncertain Graphs VLDB 2012 Keyword-aware Optimal Route Search gSketch On Query Estimation in Graph Streams A Data-Based Approach to Social Influence Maximization Scalable K-Means++ Fast and Exact Top-k Search for Random Walk with Restart VLDB 2013 iRoad A Framework For Scalable Predictive Query Processing On Road Networks Top-K Nearest Keyword Search on Large Graphs Memory Efficient Minimum Substring Partitioning Piggybacking on Social Networks Streaming Algorithms for k-core Decomposition VLDB 2014 More is Simpler Effectively and Efficiently Assessing Node Pair ... On k-Path Covers and their Applications Crowdsourcing Algorithms for Entity Resolution VLDB 2015 Viral Marketing Meets Social Advertising Ad Allocation with Minimum Regret Online Topic-Aware Influence Maximization Real-time Targeted Influence Maximization for Online Advertisements VLDB 2016 Fast Algorithm for the Lasso based L1-Graph Construction Online Entity Resolution Using an Oracle VLDB 2017 Revenue Maximization in Incentivized Social Advertising Real-Time Influence Maximization on Dynamic Social Streams Revisiting the Stop-and-Stare Algorithms for Influence Maximization ACM International Conference on Information and Knowledge Management CIKM 2008 Mining Social Networks Using Heat Diffusion Processes for Marketing ... The query-flow graph model and applications CIKM 2009 Frequent Subgraph Pattern Mining on Uncertain Graph Data CIKM 2011 Suggesting Ghost Edges for a Smaller World CIKM 2012 Delineating Social Network Data Anonymization via Random Edge Perturbation ✔Gelling, and Melting, Large Graphs by Edge Manipulation CIKM 2013 StaticGreedy Solving the Scalability-Accuracy Dilemma in Influence Maximization Personalized Influence Maximization on Social Networks Probabilistic Solutions of Influence Propagation on Networks Efficiently Anonymizing Social Networks with Reachability Preservation Overlapping Community Detection Using Seed Set Expansion CIKM 2014 Pushing the Envelope in Graph Compression CIKM 2015 Top-k Reliable Edge Colors in Uncertain Graphs International Conference on Extending Database Technology EDBT 2011 Efficient Discovery of Frequent Subgraph Patterns in Uncertain Graph Databases EDBT 2013 CINEMA Conformity-Aware Greedy Algorithm for Influence Maximization in ... EDBT 2014 Online Topic-aware Influence Maximization Queries Privacy Preserving Estimation of Social Influence ✔Fast Reliability Search in Uncertain Graphs EDBT 2015 Identifying Converging Pairs of Nodes on a Budget International Conference on Database Systems for Advanced Applications DASFAA 2011 BMC An Efficient Method to Evaluate Probabilistic Reachability Queries DASFAA 2016 Triangle-Based Representative Possible Worlds of Uncertain Graphs ウェブ + ... International World Wide Web Conference WWW 2003 Extrapolation Methods for Accelerating PageRank Computations WWW 2004 The Effect of the Back Button in a Random Walk Application for PageRank RandomSurfer with Back Step Propagation of Trust and Distrust WWW 2005 BackRank an Alternative for PageRank? WWW 2007 Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and ... WWW 2008 Fast Algorithms for Top-k Personalized PageRank Queries WWW 2009 Towards Context-Aware Search by Learning A Very Large Variable Length Hidden ... WWW 2010 Sampling Community Structure Stochastic Models for Tabbed Browsing Tracking the Random Surfer Empirically Measured Teleportation Parameters in ... WWW 2011 Limiting the Spread of Misinformation in Social Networks Estimating Sizes of Social Networks via Biased Sampling CELF++ Optimizing the Greedy Algorithm for Influence Maximization in Social ... WWW 2012 The Role of Social Networks in Information Diffusion Analyzing Spammer s Social Networks for Fun and Profit Human Wayfinding in Information Networks Optimizing Budget Allocation Among Channels and Influencers Recommendations to Boost Content Spread in Social Networks WWW 2013 Subgraph Frequencies Mapping the Empirical and Extremal Geography of Large ... Estimating Clustering Coefficients and Size of Social Networks via Random Walk Spectral Analysis of Communication Networks Using Dirichlet Eigenvalues WWW 2014 How to Influence People with Partial Incentives An Upper Bound based Greedy Algorithm for Mining Top-k Influential Nodes in ... ポスター Maximizing the Long-term Integral Influence in Social Networks Under the ... ポスター WWW 2015 Path Sampling A Fast and Provable Method for Estimating 4-Vertex Subgraph ... ✔The K-clique Densest Subgraph Problem ASIM A Scalable Algorithm for Influence Maximization under the Independent ... ✔Scalable Methods for Adaptively Seeding a Social Network WWW 2017 Why Do Cascade Sizes Follow a Power-Law? Exact Computation of Influence Spread by Binary Decision Diagrams ACM International Conference on Web Search and Data Mining WSDM 2010 TwitterRank Finding Topic-sensitive Influential Twitterers Learning Influence Probabilities In Social Networks WSDM 2013 On the Streaming Complexity of Computing Local Clustering Coefficients Influence Diffusion Dynamics and Influence Maximization in Social Networks ... From Machu_Picchu to rafting the urubamba river Anticipating information ... WSDM 2015 Negative Link Prediction in Social Media On Integrating Network and Community Discovery The Power of Random Neighbors in Social Networks International Conference on Weblogs and Social Media ICWSM 2010 ICWSM - A Great Catchy Name Semi-Supervised Recognition of Sarcastic ... ICWSM 2011 4chan and /b/ An Analysis of Anonymity and Ephemerality in a Large Online ... 人工知能 + ... AAAI Conference on Artificial Intelligence AAAI 2007 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2008 Minimizing the Spread of Contamination by Blocking Links in a Network AAAI 2010 EWLS A New Local Search for Minimum Vertex Cover AAAI 2011 Simulated Annealing Based Influence Maximization in Social Networks Nonnegative Spectral Clustering with Discriminative Regularization AAAI 2012 Exacting Social Events for Tweets Using a Factor Graph Time-Critical Influence Maximization in Social Networks with Time-Delayed ... Two New Local Search Strategies for Minimum Vertex Cover AAAI 2013 Sensitivity of Diffusion Dynamics to Network Uncertainty Spectral Rotation versus K-Means in Spectral Clustering Fast and Exact Top-k Algorithm for PageRank workshop Negative Influence Minimizing by Blocking Nodes in Social Networks AAAI 2014 New Models for Competitive Contagion Influence Maximization with Novelty Decay in Social Networks Rounded Dynamic Programming for Tree-Structured Stochastic Network Design Theory of Cooperation in Complex Social Networks AAAI 2015 Two Weighting Local Search for Minimum Vertex Cover AAAI 2016 Approximate K-Means++ in Sublinear Time AAAI 2018 Risk-Sensitive Submodular Optimization International Joint Conference on Artificial Intelligence IJCAI 2001 Link Analysis, Eigenvectors and Stability IJCAI 2009 Efficient Estimation of Influence Functions for SIS Model on Social Networks IJCAI 2011 Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph IJCAI 2015 Influence Maximization in Big Networks An Incremental Algorithm for ... Non-monotone Adaptive Submodular Maximization IJCAI 2017 Robust Quadratic Programming for Price Optimization International Conference on Artificial Intelligence and Statistics AISTATS 2012 On Bisubmodular Maximization AISTATS 2018 Random Warping Series A Random Features Method for Time-Series Embedding International Workshop on Internet and Network Economics WINE 2007 Competitive Influence Maximization in Social Networks A Note on Maximizing the Spread of Influence in Social Networks WINE 2010 Threshold Models for Competitive Influence in Social Networks IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology WI-IAT 2009 From Dango to Japanese Cakes Query Reformulation Models and Patterns WI-IAT 2014 Lazy Walks Versus Walks with Backstep Flavor of PageRank Conference on Uncertainty in Artificial Intelligence UAI 2010 Maximizing the Spread of Cascades Using Network Design International Conference on Antonomous Agents and Multiagent Sytems AAMAS 2015 Dynamic Influence Maximization Under Increasing Returns to Scale AAMAS 2016 Robust Influence Maximization (Lowalekar+) KES (International Conference on Knowledge-Based Intelligent Information and Engineering Systems) Prediction of Information Diffusion Probabilities for Independent Cascade Model ISMIS (International Conference on Foundations of Intelligent Systems) Learning Diffusion Probability based on Node Attributes in Social Networks 機械学習 + ... Conference on Neural Information Processing Systems NIPS 2003 Learning with Local and Global Consistency NIPS 2004 An Application of Boosting to Graph Classification NIPS 2009 Random Walks with Random Projections NIPS 2013 http //connpass.com/event/4728/ Scalable Influence Estimation in Continuous-Time Diffusion Networks Distributed Representations of Words and Phrases and their Compositionality DeViSE A Deep Visual-Semantic Embedding Model A Gang of Bandits Similarity Component Analysis One-shot learning by inverting a compositional causal process Inverse Density as an Inverse Problem The Fredholm Equation Approach Approximate Bayesian Image Interpretation using Generative Probabilistic ... Playing Atari with Deep Reinforcement Learning Scalable kernels for graphs with continuous attributes More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server NIPS 2014 Tight Bounds for Influence in Diffusion Networks and Application to Bond ... NIPS 2015 A Structural Smoothing Framework For Robust Graph-Comparison Anytime Influence Bounds and the Explosive Behavior of Continuous-Time ... Learnability of Influence in Networks A Submodular Framework for Graph Comparison https //stanford.edu/~jugander/NetworksNIPS2015/ ワークショップ NIPS 2016 Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Fast and Provably Good Seedings for k-Means Maximizing Influence in an Ising Network A Mean-Field Optimal Solution Budgeted stream-based active learning via adaptive submodular maximization Computing and maximizing influence in linear threshold and triggering models The Power of Optimization from Samples NIPS 2017 Stochastic Submodular Maximization The Case of Coverage Functions Robust Optimization for Non-Convex Objectives The Importance of Communities for Learning to Influence International Conference on Machine Learning ICML 2003 ✔Marginalized Kernels Between Labeled Graphs ICML 2011 Uncovering the Temporal Dynamics of Diffusion Networks Preserving Personalized Pagerank in Subgraphs ICML 2012 Influence Maximization in Continuous Time Diffusion Networks ICML 2014 Efficient Label Propagation ICML 2015 ✔Yinyang K-Means A Drop-In Replacement of the Classic K-Means with ... ACML (Asian Conference on Machine Learning) 2009 Learning Continuous-Time Information Diffusion Model for Social Behavioral ... 高性能計算 + ... IEEE International Parallel & Distributed Processing Symposium IPDPS 2016 Rabbit Order Just-in-time Parallel Reordering for Fast Graph Analysis PDPTA (International Conference on Parallel and Distributed Processing Techniques and Applications) Latent Feature Independent Cascade Model for Social Propagation 通信ネットワーク + ... IEEE International Conference on Computer Communications INFOCOM 2007 On a Routing Problem Within Probabilistic Graphs ... INFOCOM 2012 Approximate Convex Decomposition Based Localization in Wireless Sensor Networks INFOCOM 2013 2.5K-Graphs from Sampling to Generation Maximizing Submodular Set Function with Connectivity Constraint Theory and ... A Graph Minor Perspective to Network Coding Connecting Algebraic Coding ... INFOCOM 2014 Information Diffusion in Mobile Social Networks The Speed Perspective A General Framework of Hybrid Graph Sampling for Complex Network Analysis INFOCOM 2015 Assessing Attack Vulnerability in Networks with Uncertainty INFOCOM 2016 Cost-aware Targeted Viral Marketing in Billion-scale Networks INFOCOM 2017 Why approximate when you can get the exact? Optimal Targeted Viral Marketing ... WASA (Wireless Algorithms, Systems, and Applications) Minimum-Cost Information Dissemination in Social Networks 情報検索 + ... ACM International Conference on Research and Development in Information Retrieval SIGIR 2014 The Role of Network Distance in LinkedIn People Search Influential Nodes Selection A Data Reconstruction Perspective IMRank Influence Maximization via Finding Self-Consistent Ranking 自然言語処理 + ... Meeting of the Association for Computational Linguistics ACL 2011 Word Alignment via Submodular Maximization over Matroids ACL 2013 A user-centric model of voting intention from Social Media グラフィクス・ビジョン・HCI + ... ACM SIGCHI Conference on Human Factors in Computing Systems IEEE Conference on Computer Vision and Pattern Recognition CVPR 2014 Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation superpixelでグラフを小さくして画像分割とかを効率化 SBP (International Workshop on Social Computing and Behavioral Modeling) 2009 Finding Influential Nodes in a Social Network from Information Diffusion Data Manuscript+Technical report Random-walk domination in large graphs problem definitions and fast solutions Lazier Than Lazy Greedy ✔A Fast and Provable Method for Estimating Clique Counts Using Turan s Theorem ジャーナル トップジャーナル KAIS (Knowledge and Information Systems) Efficient algorithms for influence maximization in social networks IPL (Information Processing Letters) A Fast and Practical Bit-Vector Algorithm for the Longest Common Subsequence ... Internet Mathematics Link Evolution Analysis and Algorithms Towards Scaling Fully Personalized PageRank Algorithms, Lower Bounds, and ... TKDD (Transactions on Knowledge Discovery from Data) 2009 Blocking Links to Minimize Contamination Spread in a Social Network TKDE 2013 Clustering Large Probabilistic Graphs 普通のジャーナル Computational Social Networks Efficient influence spread estimation for influence maximization under the ... Computers and Mathematics with Applications A practical bounding algorithm for computing two-terminal reliability based ... Dynamics of Information Systems Algorithmic Approaches Minimum-Risk Maximum Clique Problem Information Sciences Super mediator - A new centrality measure of node importance for information ... Minimizing the expected complete influence time of a social network Maximizing the spread of influence ranking in social networks 連続時間マルコフ連鎖を取り入れたICモデル JCO (Journal of Combinatorial Optimization) 2012 The complexity of influence maximization problem in the deterministic linear ... JSAC (IEEE Journal on Selected Areas in Communications) 2013 On Budgeted Influence Maximization in Social Networks SNAM (Social Network Analysis and Mining) 2012 On minimizing budget and time in influence propagation over social networks TPDS (IEEE Transactions on Parallel and Distributed Systems) IMGPU GPU-Accelerated Influence Maximization in Large-Scale Social Networks フォーカス外 Maximizing the Extent of Spread in a Dynamic Network ICEC (International Conference on Electronic Commerce) Maximizing Influence in a Competitive Social Network A Follower s Perspective WISE 2013 A Novel and Model Independent Approach for Efficient Influence Maximization ... 国内会議 人工知能学会 JSAI Resampling-based Predictive Simulation for Identifying Influential Nodes ... Finding Important Users for Information Diffusion Influence analysis of information diffusion focusing on directed networks Proposal of AIDM Agent-based Information Diffusion Model Predicting Japanese General Election in 2013 with Twitter Considering ... Which Targets to Contact First to Maximize Influence over Social Network 他分野 Econometrica The Network Origins of Aggregate Fluctuations PLoS ONE Social Network Sensors for Early Detection of Contagious Outbreaks Proceedings of the National Academy of Sciences PNAS Dynamic social networks promote cooperation in experiments with humans Spectral Redemption Clustering Sparse Networks Physical Review Letters First Passage Time for Random Walks in Heterogeneous Networks Adaptation and Optimization of Biological Transport Networks Locating the Source of Diffusion in Large-Scale Network Enhanced Flow in Small-World Networks Science Quantifying Long-Term Scientific Impact Control Profiles of Complex Networks Nature Communications Griffiths phases and the stretching of criticality in brain networks A scaling law for random walks on networks Influence maximization in complex networks through optimal percolation 2024-04-23 23 09 41 (Tue)