Noncooperative Information Diffusion in Online Social Networks Under the Independent Cascade Model
In this paper, we present the first detailed analysis of influence maximization in noncooperative social networks under the Independent Cascade Model (ICM). We propose a new influence model based on the ICM and prove the approximation guarantees for influence maximization in noncooperative settings....
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Veröffentlicht in: | IEEE transactions on computational social systems 2017-09, Vol.4 (3), p.150-162 |
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description | In this paper, we present the first detailed analysis of influence maximization in noncooperative social networks under the Independent Cascade Model (ICM). We propose a new influence model based on the ICM and prove the approximation guarantees for influence maximization in noncooperative settings. We structure the influence diffusion into two stages, namely, seed node selection and influence diffusion. In the former, we introduce a modified hierarchy-based seed node selection strategy, which can take node noncooperation into consideration. In the latter, we propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey- Clarke-Groves (VCG)-like scheme to incentivise cooperation. Then, we study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. We evaluate our proposed schemes on large coauthorship networks, and the results show that our seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative. |
doi_str_mv | 10.1109/TCSS.2017.2719056 |
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K. ; Kuang Xu</creator><creatorcontrib>Yile Yang ; Zhiyi Lu ; Li, Victor O. K. ; Kuang Xu</creatorcontrib><description>In this paper, we present the first detailed analysis of influence maximization in noncooperative social networks under the Independent Cascade Model (ICM). We propose a new influence model based on the ICM and prove the approximation guarantees for influence maximization in noncooperative settings. We structure the influence diffusion into two stages, namely, seed node selection and influence diffusion. In the former, we introduce a modified hierarchy-based seed node selection strategy, which can take node noncooperation into consideration. In the latter, we propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey- Clarke-Groves (VCG)-like scheme to incentivise cooperation. Then, we study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. We evaluate our proposed schemes on large coauthorship networks, and the results show that our seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative.</description><identifier>ISSN: 2329-924X</identifier><identifier>EISSN: 2329-924X</identifier><identifier>EISSN: 2373-7476</identifier><identifier>DOI: 10.1109/TCSS.2017.2719056</identifier><identifier>CODEN: ITCSGL</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithm design and analysis ; Cooperative ; Diffusion processes ; Economic models ; Game theory ; Greedy algorithms ; Incentive schemes ; influence maximization ; Information dissemination ; Maximization ; Peer-to-peer computing ; Resource management ; social network ; Social network services ; Social networks</subject><ispartof>IEEE transactions on computational social systems, 2017-09, Vol.4 (3), p.150-162</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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K.</creatorcontrib><creatorcontrib>Kuang Xu</creatorcontrib><title>Noncooperative Information Diffusion in Online Social Networks Under the Independent Cascade Model</title><title>IEEE transactions on computational social systems</title><addtitle>TCSS</addtitle><description>In this paper, we present the first detailed analysis of influence maximization in noncooperative social networks under the Independent Cascade Model (ICM). We propose a new influence model based on the ICM and prove the approximation guarantees for influence maximization in noncooperative settings. We structure the influence diffusion into two stages, namely, seed node selection and influence diffusion. In the former, we introduce a modified hierarchy-based seed node selection strategy, which can take node noncooperation into consideration. In the latter, we propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey- Clarke-Groves (VCG)-like scheme to incentivise cooperation. Then, we study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. We evaluate our proposed schemes on large coauthorship networks, and the results show that our seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative.</description><subject>Algorithm design and analysis</subject><subject>Cooperative</subject><subject>Diffusion processes</subject><subject>Economic models</subject><subject>Game theory</subject><subject>Greedy algorithms</subject><subject>Incentive schemes</subject><subject>influence maximization</subject><subject>Information dissemination</subject><subject>Maximization</subject><subject>Peer-to-peer computing</subject><subject>Resource management</subject><subject>social network</subject><subject>Social network services</subject><subject>Social networks</subject><issn>2329-924X</issn><issn>2329-924X</issn><issn>2373-7476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhhdRsNT-APES8Nw6SfYjOcr6VajtoS14W7KbCW7dJmuyVfz37tIiXmbew_POwBNF1xRmlIK82-Tr9YwBzWYsoxKS9CwaMc7kVLL47fxfvowmIewAgLIkyRiMonLpbOVci1519ReSuTXO7_vsLHmojTmEIdWWrGxTWyRrV9WqIUvsvp3_CGRrNXrSvQ9NjS32w3YkV6FSGsmr09hcRRdGNQEnpz2Otk-Pm_xlulg9z_P7xbRiknfTlFepMmAgZlqknGtJlUwVTTNISlGCYUiVqMCUMZMapIDMoDbMcMaNSikfR7fHu613nwcMXbFzB2_7lwWVPE4SwYTsKXqkKu9C8GiK1td75X8KCsVgsxhsFoPN4mSz79wcOzUi_vECQGRC8F_GEXE7</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Yile Yang</creator><creator>Zhiyi Lu</creator><creator>Li, Victor O. K.</creator><creator>Kuang Xu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-7607-0828</orcidid></search><sort><creationdate>20170901</creationdate><title>Noncooperative Information Diffusion in Online Social Networks Under the Independent Cascade Model</title><author>Yile Yang ; Zhiyi Lu ; Li, Victor O. K. ; Kuang Xu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-63c6af0f042d8633d91a96a16705b8b0f2e1a8c0fb429d09807fedf2f323fa613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithm design and analysis</topic><topic>Cooperative</topic><topic>Diffusion processes</topic><topic>Economic models</topic><topic>Game theory</topic><topic>Greedy algorithms</topic><topic>Incentive schemes</topic><topic>influence maximization</topic><topic>Information dissemination</topic><topic>Maximization</topic><topic>Peer-to-peer computing</topic><topic>Resource management</topic><topic>social network</topic><topic>Social network services</topic><topic>Social networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yile Yang</creatorcontrib><creatorcontrib>Zhiyi Lu</creatorcontrib><creatorcontrib>Li, Victor O. K.</creatorcontrib><creatorcontrib>Kuang Xu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on computational social systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yile Yang</au><au>Zhiyi Lu</au><au>Li, Victor O. K.</au><au>Kuang Xu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Noncooperative Information Diffusion in Online Social Networks Under the Independent Cascade Model</atitle><jtitle>IEEE transactions on computational social systems</jtitle><stitle>TCSS</stitle><date>2017-09-01</date><risdate>2017</risdate><volume>4</volume><issue>3</issue><spage>150</spage><epage>162</epage><pages>150-162</pages><issn>2329-924X</issn><eissn>2329-924X</eissn><eissn>2373-7476</eissn><coden>ITCSGL</coden><abstract>In this paper, we present the first detailed analysis of influence maximization in noncooperative social networks under the Independent Cascade Model (ICM). We propose a new influence model based on the ICM and prove the approximation guarantees for influence maximization in noncooperative settings. We structure the influence diffusion into two stages, namely, seed node selection and influence diffusion. In the former, we introduce a modified hierarchy-based seed node selection strategy, which can take node noncooperation into consideration. In the latter, we propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey- Clarke-Groves (VCG)-like scheme to incentivise cooperation. Then, we study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. We evaluate our proposed schemes on large coauthorship networks, and the results show that our seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TCSS.2017.2719056</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-7607-0828</orcidid></addata></record> |
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subjects | Algorithm design and analysis Cooperative Diffusion processes Economic models Game theory Greedy algorithms Incentive schemes influence maximization Information dissemination Maximization Peer-to-peer computing Resource management social network Social network services Social networks |
title | Noncooperative Information Diffusion in Online Social Networks Under the Independent Cascade Model |
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