Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2011 SIAM International Conference on Data Mining (SDM)Influence Maximization in Social Networks When Negative Opinions May Emerge and PropagateWei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincon, Xiaorui Sun, Yajun Wang, Wei Wei, and Yifei YuanWei ChenMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Alex CollinsMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Rachel CummingsMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Te KeMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Zhenming LiuMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, David RinconMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Xiaorui SunMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Yajun WangMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, Wei WeiMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this author, and Yifei YuanMicrosoft Research Asia, China.Google Inc., U.S.A.University of Southern California, U.S.A.University of California at Berkeley, U.S.A.Harvard University, U.S.A.Universitat Politècnica de Catalunya, Spain.Shanghai Jiao Tong University, China.Microsoft Research Asia, China.Carnegie Mellon University, U.S.A.University of Pennsylvania, U.S.A.Search for more papers by this authorpp.379 - 390Chapter DOI:https://doi.org/10.1137/1.9781611972818.33PDFBibTexSections ToolsAdd to favoritesDownload CitationsTrack CitationsEmail SectionsAboutAbstract Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 – 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to compute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster. Previous chapter Next chapter RelatedDetails Published:2011ISBN:978-0-89871-992-5eISBN:978-1-61197-281-8 https://doi.org/10.1137/1.9781611972818Book Series Name:ProceedingsBook Code:PRDT11Book Pages:1-1015Key words:influence maximization, social networks, negative opinions, independent cascade model