Computer Science ›› 2014, Vol. 41 ›› Issue (1): 48-53.

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Survey of Influence Analysis for Social Networks

DING Zhao-yun,JIA Yan,ZHOU Bin and TANG Fu   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The Internet is gradually evolved into a ubiquitous computing platform and information dissemination platform.Emergence and rapid development of online social networking sites,micro blogging,blogs,forums,wikis and social networking applications,make the form for the human to use the Internet to produce profound changes from simple information search and Web browser to construction and maintenance of online socialration information creation,exchange and sharing based on social relations.Of interaction between individuals in the social network influence,the influence of the social network is mainly dependent on the strength of the relationship between the individual,the network distance between individuals,the timing factor,as well as network characteristics and individual characteristics.Influential analysis technology related research includes individual impact strength measurement technology,individual influence and the diffusion mechanisms of influence.

Key words: Social networks,Data mining,Influence,Opinion leaders

[1] Gladwell M.The Tipping Point:How Little Things Can Make a Big Difference [M].New York:Little Brown,2000
[2] Berry J,Keller E.The Influentials:One American in Ten Tells the Other Nine How to Vote,Where to Eat,and What to Buy [M].New York:The Free Press,2003
[3] Katz E,Lazarsfeld P.Personal Influence:The Part Played byPeople in the Flow of Mass Communications [M].New York:The Free Press,1955
[4] Rogers E M.Diffusion of Innovations [M].New York:The Free Press,1962
[5] Cialdini R B.Influence:Science and Practice [M].Boston:Allyn and Bacon,2003
[6] Aggarwal C C.Social Network Data Analytics [M].New York:Springer,2012
[7] Goyal A,Bonchi F,Lakshmanan L V S.Learning influence probabilities in social networks [C]∥the 3rd ACM International Conference on Web Search and Data Mining (WSDM’10).New York,USA,February 2010:241-250
[8] Xiang R,Neville J,Rogati M.Modeling relationship strength in online social networks [C]∥the 19th International Conference on World Wide Web (WWW’10).Raleigh,USA,April 2010:981-990
[9] Aral S,Walker D.Identifying influential and susceptible mem-bers of social networks [J].Science,2012,337(6092):337-341
[10] Blei D M,Ng A Y,Jordan M I.Latent Dirichlet Allocation [J].Journal of Machine Learning Research,2003,3:993-1022
[11] Dietz L,Bickel S,Scheffer T.Unsupervised Prediction of Cita-tion Influences [C]∥the 24th International Conference on Machine Learning (ICML’07).Corvallis,USA,June 2007:233-240
[12] Liu L,Tang J,Han J,et al.Mining topic-level influence in heterogeneous networks [C]∥the 19th ACM International Confe-rence on Information and Knowledge Management (CIKM’10).Toronto,Canada,October 2010:199-208
[13] Liu L,Tang J,Han J,et al.Learning influence from heterogeneous social networks [J].Data Mining and Knowledge Discove-ry,2012,25(3):511-544
[14] Shaparenko B,Joachims T.Information genealogy:Uncoveringthe flow of ideas in non-hyperlinked document databases [C]∥the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07).San Jose,USA,August 2007:619-628
[15] Gerrish S M,Blei D M.A language-based approach to measuring scholarly impact [C]∥the 26th International Conference on Machine Learning (ICML’10).Haifa,Israel,November 2010:375-382
[16] Tang J,Sun J,Wang C,et al.Social influence analysis in large-scale networks [C]∥the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09).Paris,France,June 2009:807-816
[17] Ding Z Y,Jia Y,Zhou B,et al.An influence strength measurement via time-aware probabilistic generative model for microblogs[C]∥the 15th Asia-Pacific Web Conference.Sydney,Australia,April 2013
[18] Wasserman S,Faust K.Social Network Analysis:Methods and Applications [M].London:Cambridge University Press,1994
[19] Aggarwal C,Wang H.Managing and Mining Graph Data [M].New York:Springer,2010
[20] 乔少杰,唐常杰,彭京,等.基于个性特征仿真邮件分析系统挖掘犯罪网络核心[J].计算机学报,2008,31(10):1795-1803
[21] Cha M,Haddadi H,Benevenuto F,et al.Measuring user influence in Twitter:The million follower fallacy [C]∥the 4th International AAAI Conference on Weblogs and Social Media (ICWSM’10).Washington,USA,May 2010:10-17
[22] Pal A,Counts S.Identifying topical authorities in microblogs[C]∥the 4th ACM International Conference on Web Search and Data Mining (WSDM’11).Hong Kong,China,February 2011:45-54
[23] Brandes U.A faster algorithm for betweenness centrality [J].Journal of Mathematical Sociology,2001,25:163-177
[24] Quercia D,Capra L,Crowcroft J.The social world of Twitter:Topics,geography,and emotions [C]∥the 6th International AAAI Conference on Weblogs and Social Media (ICWSM’12).Dublin,Ireland,June 2012:298-305
[25] Kleinberg J M.Authoritative sources in a hyperlinked environment [J].Journal of the ACM,1999,46(5):604-632
[26] Romero D M,Galuba W,Asur S,et al.Influence and passivity in social media [C]∥the 20th International Conference Companion on World Wide Web (WWW’11).Hyderabad,India,March 2011:113-114
[27] Page L,Brin S,Motwani R,et al.The PageRank citation ranking:bringing order to the Web [R/OL].http://ilpubs.stanford.edu:8089/422/,1999
[28] Tunkelang D.A Twitter analog to PageRank [EB/OL].http://thenoisychannel.com/2009/01/13/a_twitter_analog_to_pagerank/,2009
[29] Haveliwala T,Sepandar K,Glen J.An analytical comparsion of approaches to personalizing PageRank [R/OL].http://ilpubs.stanford.edu:8089/596/,2003
[30] Agarwal N,Liu H,Lei T,et al.Identifying the influential bloggers in a community [C]∥Proc of the 1th ACM International Conference on Web Search and Data Mining.New York,NY:ACM,2008:207-217
[31] Cai Y,Chen Y.Mining influential bloggers:From general to domain specific [C]∥Proc of the 13th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems.Berlin:Springer,2009:447-454
[32] Hui P,Gregory M.Quantifying sentiment and influence inblogspaces [C]∥Proc of the 1st Workshop on Social Media Analytics.New York,NY:ACM,2010:53-61
[33] Weng J,Lim E P,Jiang J,et al.TwitterRank:Finding topic-sensitive influential twitterers [C]∥the 3rd ACM International Conference on Web Search and Data Mining (WSDM’10).New York,USA,February 2010:261-270
[34] Li D,Shuai X,Sun G,et al.Mining topic-level opinion influence in microblog [C]∥the 21st ACM International Conference on Information and Knowledge Management (CIKM’12).Maui,USA,October 2012:1562-1566
[35] Song X,Yun C,Hino K,et al.Identifying opinion leaders in the blogosphere [C]∥the 16th ACM International Conference on Information and Knowledge Management (CIKM’07).Lisboa,Portugal,November 2007:971-974
[36] Ding Z Y,Jia Y,Zhou B,et al.Mining topical influencers based on the multi-relational network in micro-blogging sites [J].China Communications,2013,10(1):93-104
[37] Kempe D,Kleinberg J,Tardos E.Maximizing the spread of influence through a social network [C]∥the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’03).Washington,USA,August 2003:137-146
[38] Manuel G R,Leskovec J,Krause A.Inferring networks of diffusion and influence [C]∥the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10).Washington,USA,July 2010:1019-1028
[39] Chen W,Wang C,Wang Y.Scalable influence maximization for prevalent viral marketing in large-scale social networks [C]∥the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10).Washington,USA,July 2010:1029-1038
[40] Wang C,Chen W,Wang Y.Scalable influence maximization for independent cascade model in large-scale social networks [J].Data Mining and Knowledge Discovery,2012,25(3):545-576
[41] Lee C,Kwak H,Park H,et al.Finding influentials based on thetemporal order of information adoption in Twitter [C]∥the 19th International Conference Companion on World Wide Web (WWW’10).Raleigh,USA,April 2010:1137-1138
[42] Gruhl D,Guha R,Liben-Nowell D,et al.Information diffusion through blogspace [C]∥Proc of the 13th International World Wide Web Conference.New York,NY:ACM,2004:43-52
[43] Java A,Kolari P,Finin T,et al.Modeling the spread of influence on the blogosphere [R].Maryland:UMBC,2006
[44] Bakshy E,Hofman J M,Mason W A,et al.Everyone’s an influencer:Quantifying influence on Twitter [C]∥the 4th ACM International Conference on Web Search and Data Mining (WSDM’11).Hong Kong,China,February 2011:65-74
[45] Kitsak M,Gallos L K,Havlin S,et al.Identification of influential spreaders in complex networks [J].Nature Physics,2010,6:888-893
[46] Aggarwal C C,Khan A,Yan X.On flow authority discovery in social networks [C]∥the 11th SIAM International Conference on Data Mining (SDM’11).Phoenix,USA,April 2011:522-533
[47] Steeg G V,Galstyan A.Information transfer in social media [C]∥the 21st International Conference on World Wide Web (WWW’12).Lyon,France,April 2012:509-518
[48] Singla P,Richardson M.Yes,there is a correlation -From social networks to personal behavior on the Web [C]∥Proc of the 17th International World Wide Web Conference.New York,NY:ACM,2008:655-664
[49] Anagnostopoulos A,Kumar R,Mahdian M.Influence and correlation in social networks [C]∥Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,NY:ACM,2008:7-15
[50] Crandall D,Cosley D,Huttenlocher D,et al.Feedback effectsbetween similarity and social influence in online communities [C]∥Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,NY:ACM,2008:160-168
[51] Kwak H,Lee C,Park H,et al.What is Twitter,A social network or a news media? [C]∥the 19th International Conference on World Wide Web (WWW’10).Raleigh,USA,April 2010:591-600

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