Abstract
Influence is a complex and subtle force that governs social dynamics and user behaviors. Understanding how users influence each other can benefit various applications, e.g., viral marketing, recommendation, information retrieval and etc. While prior work has mainly focused on qualitative aspect, in this article, we present our research in quantitatively learning influence between users in heterogeneous networks. We propose a generative graphical model which leverages both heterogeneous link information and textual content associated with each user in the network to mine topic-level influence strength. Based on the learned direct influence, we further study the influence propagation and aggregation mechanisms: conservative and non-conservative propagations to derive the indirect influence. We apply the discovered influence to user behavior prediction in four different genres of social networks: Twitter, Digg, Renren, and Citation. Qualitatively, our approach can discover some interesting influence patterns from these heterogeneous networks. Quantitatively, the learned influence strength greatly improves the accuracy of user behavior prediction.
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Anagnostopoulos A, Kumar R, Mahdian M (2008) Influence and correlation in social networks. In: KDD’08, pp 7–15
Bonacich P (1972) Factoring and weighting approaches to status scores and clique identification. J Math Sociol 2(1): 113–120
Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5): 1170–1182
Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23(3): 191–201
Chang J, Boyd-Graber J, Blei DM (2009) Connections between the lines: augmenting social networks with text. In: KDD ’09, pp 169–178
Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD ’10
Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: KDD’08, pp 160–168
Cui P, Wang F, Liu S, Ou M, Yang S (2011) Who should share what? item-level social influence prediction for users and posts ranking. In: SIGIR’11
Dietz L, Bickel S, Scheffer T (2007) Unsupervised prediction of citation influences. In: ICML’07, pp 233–240
Domingos P, Richardson M (2001) Mining the network value of customers. In: KDD’ 01, pp 57–66
Doucet A, de Freitas N, Murphy K, Russell S (2000) Rao-blackwellised particle filtering for dynamic bayesian networks. In: UAI’00, pp 176–183
Dourisboure Y, Geraci F, Pellegrini M (2007) Extraction and classification of dense communities in the web. In: WWW ’07, pp 461–470
Fowler JH, Christakis NA (2008) Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study. Br Med J 337: a2338
Gerrish S, Blei DM (2010) A language-based approach to measuring scholarly impact. In: ICML’10, pp 375–382
Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: WSDM’10, pp 207–217
Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6): 1360–1380
Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: WWW ’04, pp 491–501
Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: WWW ’04, pp 403–412
Haveliwala TH (2002) Topic-sensitive pagerank. In: WWW’02, pp 517–526
Hopcroft JE, Lou T, Tang J (2011) Who will follow you back? reciprocal relationship prediction. In: CIKM’11
Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103(3): 038702
Jeh G, Widom J (2002) Scaling personalized web search. In: WWW’02, pp 271–279
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1): 39–43
Kelman H (1958) Compliance, identification, and internalization: three processes of attitude change. J Confl Resolut 2(1): 51–60
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: KDD ’03, pp 137–146
King J (1987) A review of bibliometric and other science indicators and their role in research evaluation. J Inform Sci 13(5): 261–276
Kiss C, Bichler M (2008) Identification of influencers—measuring influence in customer networks. Decis Support Syst 46(1): 233–253
Kossinets G, Kleinberg J, Watts D (2008) The structure of information pathways in a social communication network. In: KDD ’08, pp 435–443
Krackhardt D (1992) The Strength of Strong ties: the importance of philos in networks and organization. In: Nohria N, Eccles RG (eds) Networks and organizations. Harvard Business School Press, Hershey
La Fond T, Neville J (2010) Randomization tests for distinguishing social influence and homophily effects. In: WWW ’10, pp 601–610
Liben-Nowell D, Kleinberg J (2008) Tracing information flow on a global scale using Internet chain-letter data. Proc Natl Acad Sci USA 105(12): 4633–4638
Liu L, Tang J, Han J, Jiang M, Yang S (2010) Mining topic-level influence in heterogeneous networks. In: CIKM’10, pp 199–208
Macskassy S, Provost F (2003) A simple relational classifier. In: Workshop on multi-relational data mining in conjunction with KDD’03
Nallapati RM, Ahmed A, Xing E, Cohen WW (2008) Joint latent topic models for text and citations. In: KDD ’08, pp 542–550
Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Technical Report SIDL-WP-1999-0120, Stanford University
Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: KDD ’02, pp 61–70
Rodriguez MG, Leskovec J, Krause A (2010) Inferring networks of diffusion and influence. In: KDD ’10
Scripps J, Tan P-N, Esfahanian A-H (2009) Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. In: KDD ’09, pp 747–756
Singla P, Richardson M (2008) Yes, there is a correlation: from social networks to personal behavior on the web. In: WWW ’08, pp 655–664
Sun Y, Han J, Zhao P, Yin Z, Cheng H, Wu T (2009a) RankClus: integrating clustering with ranking for heterogeneous information network analysis. In: EDBT’09
Sun Y, Yu Y, Han J (2009b) Ranking-based clustering of heterogeneous information networks with star network schema. In: KDD ’09, pp 797–806
Tan C, Tang J, Sun J, Lin Q, Wang F (2010) Social action tracking via noise tolerant time-varying factor graphs. In: KDD’10, pp 1049–1058
Tang L, Liu H (2009) Relational learning via latent social dimensions. In: KDD ’09, pp 817–826
Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. In: KDD ’09, pp 807–816
Tang J, Lou T, Kleinberg J (2012) Inferring social ties across heterogenous networks. In: WSDM’12
Whitfield J (2008) The secret of happiness: grinning on the internet. Nature. doi:10.1038/news.2008.918
Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: WWW ’10, pp 981–990
Yang T, Jin R, Chi Y, Zhu S (2009) Combining link and content for community detection: a discriminative approach. In: KDD ’09, pp 927–936
Ye J, Chen K, Wu T, Li J, Zhao Z, Patel R, Bae M, Janardan R, Liu H, Alexander G, Reiman E (2008) Heterogeneous data fusion for alzheimer’s disease study. In: KDD ’08, pp 1025–1033
Zheleva E, Sharara H, Getoor L (2009) Co-evolution of social and affiliation networks. In: KDD ’09, pp 1007–1016
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Responsible editor: Fei Wang, Hanghang Tong, Phillip Yu, Charu Aggarwal.
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Liu, L., Tang, J., Han, J. et al. Learning influence from heterogeneous social networks. Data Min Knowl Disc 25, 511–544 (2012). https://doi.org/10.1007/s10618-012-0252-3
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DOI: https://doi.org/10.1007/s10618-012-0252-3