Regularized topic-aware latent influence propagation in dynamic relational networks


On social networks, investigating how the influence is propagated is crucial in understanding the network evolution and the social impact of different topics. In previous study, the influence propagation is either modeled based on the static network structure, or the infection between two connected users is recovered from some given event cascades. Unfortunately, existing solutions are incapable of identifying the user susceptibility delivered by user generated content. In this paper, we propose RegInfoIbp, a general regularized learning framework for modeling topic-aware influence propagation in dynamic network structures. Specifically, the observed time-sequential user topic preference and user adjacency information are factorized by the prior information reflected by a user-influential bipartite relation graph. The influence propagation is approximated with a nonparametric regularized Bayesian matrix factorization model with tractable polynomial complexity. and the influential users are identified by several sampling algorithms with slightly different approximation qualities. To further model dynamic temporal evolution, we construct Markov conditional probabilistic model on the compact latent feature representation. By integrating both topic and structure information into the regularized non-parametric probabilistic learning process, RegInfoIbp is more efficient and accurate in discovering the key factors in the content and influential users in dynamic network structure. Extensive experiments demonstrate that RegInfoIbp better adapts to real data, and achieves better approximation in influence propagation over existing approaches.

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  1. 1.

  2. 2.


  1. 1.

    Aggarwal CC, Lin S, Yu PS (2012) On influential node discovery in dynamic social networks. SDM ’12, pp 636–647

  2. 2.

    Barbieri N, Bonchi F, Manco G (2012) Topic-aware social influence propagation models. ICDM ’12, pp 81–90

  3. 3.

    Bi B, Tian Y, Sismanis Y, Balmin A, Cho J (2014) Scalable topic-specific influence analysis on microblogs. WSDM ’14, pp 513–522

  4. 4.

    Borgs C, Brautbar M, Chayes J, Lucier B (2014) Maximizing social influence in nearly optimal time. SODA ’14, pp 946–957

  5. 5.

    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117

    Article  Google Scholar 

  6. 6.

    Caron F (2012) Bayesian nonparametric models for bipartite graphs. NIPS ’12, pp 2051–2059

  7. 7.

    Chen S, Fan J, Li G, Feng J, lee Tan K, Tang J (2015) Online topic aware influence maximization. In: Proceedings of VLDB Endowment, vol 8

  8. 8.

    Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. KDD ’10, pp 1029–1038

  9. 9.

    Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. KDD ’09, pp 199–208

  10. 10.

    Du N, Song L, Gomez-Rodriguez M, Zha H (2013) Scalable influence estimation in continuous-time diffusion networks. NIPS ’13, pp 3147–3155

  11. 11.

    Foulds JR, Dubois C, Asuncion AU, Butts CT, Smyth P (2011) A dynamic relational infinite feature model for longitudinal social networks. AISTATS ’11, pp 287–295

  12. 12.

    Gael JV, Teh YW, Ghahramani Z (2008) The infinite factorial hidden markov model. NIPS ’08, pp 1697–1704

  13. 13.

    Gopalan P, Ruiz FJ, Ranganath R, Blei D (2014) Bayesian nonparametric poisson factorization for recommendation systems. AISTATS ’14, pp 275–283

  14. 14.

    Goyal A, Lu W, Lakshmanan LV (2011) Celf++ optimizing the greedy algorithm for influence maximization in social networks. WWW ’11, pp 47–48

  15. 15.

    Griffiths TL, Ghahramani Z (2005) Infinite latent feature models and the indian buffet process. In: NIPS, pp 475–482

  16. 16.

    Heaukulani C, Ghahramani Z (2013) Dynamic probabilistic models for latent feature propagation in social networks. ICML ’13, pp 275–283

  17. 17.

    Huo Z, Huang X, Hu X (2018) Link prediction with personalized social influence. In: AAAI

  18. 18.

    Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. KDD ’03, pp 137–146

  19. 19.

    Kulesza A, Taskar B (2012) Determinantal point processes for machine learning. arXiv:1207.6083

  20. 20.

    Le Cam L (1960) An approximation theorem for the poisson binomial distribution. Pac J Math 10:1181–1197

    Article  Google Scholar 

  21. 21.

    Lei S, Maniu S, Mo L, Cheng R, Senellart P (2015) Online influence maximization. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 645–654

  22. 22.

    Liu L, Tang J, Han J, Jiang M, Yang S (2010) Mining topic-level influence in heterogeneous networks. CIKM ’10, pp 199–208

  23. 23.

    Liu S, Qu Q, Wang S (2018) Heterogeneous anomaly detection in social diffusion with discriminative feature discovery. Inf Sci 439-440:1–18

    Article  Google Scholar 

  24. 24.

    Liu S, Wang S (2017) Trajectory community discovery and recommendation by multi-source diffusion modeling. IEEE Trans Knowl Data Eng 29(4):898–911

    Article  Google Scholar 

  25. 25.

    Liu S, Wang S, Zhu F (2015) Structured learning from heterogeneous behavior for social identity linkage. IEEE Trans Knowl Data Eng 27(7):2005–2019

    Article  Google Scholar 

  26. 26.

    Miller K, Jordan MI, Griffiths TL (2009) Nonparametric latent feature models for link prediction. NIPS ’09, pp 1276–1284

  27. 27.

    Nguyen HT, Thai MT, Dinh TN (2016) Stop-and-stare:optimal sampling algorithms for viral marketing in billion-scale networks. In: ACM international conference on management of data (SIGMOD), pp 695–710

  28. 28.

    Pan T, Kuhnle A, Li X, Thai MT (2017) Popular topics spread faster: New dimension for influence propagation in online social networks. arXiv:1702.01844

  29. 29.

    Phan N, Ebrahimi J, Dou D, Kil D, Piniewski B (2015) Topic-aware physical activity propagation with temporal dynamics in a health social network. ACM transactions on intelligent systems and technology

  30. 30.

    Qu Q, Liu S, Yang B, Jensen CS (2014) Efficient top-k spatial locality search for co-located spatial web objects. In: IEEE MDM, pp 269–278

  31. 31.

    Qu Q, Liu S, Zhu F, Jensen CS (2016) Efficient online summarization of large-scale dynamic networks. IEEE Trans Knowl Data Eng 28(12):3231–3245

    Article  Google Scholar 

  32. 32.

    Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. KDD ’02, pp 61–70

  33. 33.

    Rodriguez MG, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. ICML ’12, pp 313–320

  34. 34.

    Scott SL (2002) Bayesian methods for hidden markov models: Recursive computing in the 21st century. J Am Stat Assoc 97:337–351

    Article  Google Scholar 

  35. 35.

    Song D, Meyer DA, Tao D (2015) Efficient latent link recommendation in signed networks. In KDD, pp 1105–1114

  36. 36.

    Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. KDD ’09, pp 807–816

  37. 37.

    Tang Y, Xiao X, maximization Y. S. h. i. (2014) Influence Near-optimal time complexity meets practical efficiency. SIGMOD ’14, pp 75–86

  38. 38.

    Tong G, Wu W, Tang S, Du DZ (2017) Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans Networking 25(1):112–125

    Article  Google Scholar 

  39. 39.

    Wang B, Chen G, Fu L, Song L, Wang X (2017) DRIMUX dynamic rumor influence minimization with user experience in social networks. IEEE Trans Knowl Data Eng PP(99):1–1

    Google Scholar 

  40. 40.

    Wang C, Tang J, Sun J, Han J (2011) Dynamic social influence analysis through time-dependent factor graphs. ASONAM ’11, pp 239–246

  41. 41.

    Weng J, Lim E-P, Jiang J, He Q (2010) Twitterrank: Finding topic-sensitive influential twitterers. WSDM ’10, pp 261–270

  42. 42.

    Wood F, Griffiths TL, Ghahramani Z (2006) A non-parametric Bayesian method for inferring hidden causes. UAI ’06, pp 536–543

  43. 43.

    Zhan Q, Zhang J, Wang S, Yu P, Xie J (2015) Influence maximization across partially aligned heterogenous social networks. In: PAKDD, pp 58–69

  44. 44.

    Zhang J, Yu PS (2014) Link prediction across heterogeneous social networks: A survey

  45. 45.

    Zhang J, Yu PS (2015) Integrated anchor and social link predictions across partially aligned social networks. In: IJCAI

  46. 46.

    Zheng W, Kveton B, Valko M, Vaswani S (2017) Online influence maximization under independent cascade model with semi-bandit feedback. In: NIPS

  47. 47.

    Zhuang H, Sun Y, Tang J, Zhang J, Sun X (2013) Influence maximization in dynamic social networks. ICDM ’13, pp 1313–1318

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This work was supported in part by National Natural Science Foundation of China: 61672497, 61620106009, 61771457, 61732007, U1636214 and 61836002, in part by National Basic Research Program of China (973 Program): 2015CB351800, and in part by Key Research Program of Frontier Sciences of CAS: QYZDJ-SSW-SYS013.

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Correspondence to Liang Li.

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Wang, S., Li, L., Yang, C. et al. Regularized topic-aware latent influence propagation in dynamic relational networks. Geoinformatica 23, 329–352 (2019).

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  • Bayesian nonparametric matrix factorization
  • Influence propagation
  • Dynamic relational networks