Abstract
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.
Similar content being viewed by others
References
Aggarwal CC, Lin S, Yu PS (2012) On influential node discovery in dynamic social networks. SDM ’12, pp 636–647
Barbieri N, Bonchi F, Manco G (2012) Topic-aware social influence propagation models. ICDM ’12, pp 81–90
Bi B, Tian Y, Sismanis Y, Balmin A, Cho J (2014) Scalable topic-specific influence analysis on microblogs. WSDM ’14, pp 513–522
Borgs C, Brautbar M, Chayes J, Lucier B (2014) Maximizing social influence in nearly optimal time. SODA ’14, pp 946–957
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117
Caron F (2012) Bayesian nonparametric models for bipartite graphs. NIPS ’12, pp 2051–2059
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
Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. KDD ’10, pp 1029–1038
Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. KDD ’09, pp 199–208
Du N, Song L, Gomez-Rodriguez M, Zha H (2013) Scalable influence estimation in continuous-time diffusion networks. NIPS ’13, pp 3147–3155
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
Gael JV, Teh YW, Ghahramani Z (2008) The infinite factorial hidden markov model. NIPS ’08, pp 1697–1704
Gopalan P, Ruiz FJ, Ranganath R, Blei D (2014) Bayesian nonparametric poisson factorization for recommendation systems. AISTATS ’14, pp 275–283
Goyal A, Lu W, Lakshmanan LV (2011) Celf++ optimizing the greedy algorithm for influence maximization in social networks. WWW ’11, pp 47–48
Griffiths TL, Ghahramani Z (2005) Infinite latent feature models and the indian buffet process. In: NIPS, pp 475–482
Heaukulani C, Ghahramani Z (2013) Dynamic probabilistic models for latent feature propagation in social networks. ICML ’13, pp 275–283
Huo Z, Huang X, Hu X (2018) Link prediction with personalized social influence. In: AAAI
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. KDD ’03, pp 137–146
Kulesza A, Taskar B (2012) Determinantal point processes for machine learning. arXiv:1207.6083
Le Cam L (1960) An approximation theorem for the poisson binomial distribution. Pac J Math 10:1181–1197
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
Liu L, Tang J, Han J, Jiang M, Yang S (2010) Mining topic-level influence in heterogeneous networks. CIKM ’10, pp 199–208
Liu S, Qu Q, Wang S (2018) Heterogeneous anomaly detection in social diffusion with discriminative feature discovery. Inf Sci 439-440:1–18
Liu S, Wang S (2017) Trajectory community discovery and recommendation by multi-source diffusion modeling. IEEE Trans Knowl Data Eng 29(4):898–911
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
Miller K, Jordan MI, Griffiths TL (2009) Nonparametric latent feature models for link prediction. NIPS ’09, pp 1276–1284
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
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
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
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
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
Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. KDD ’02, pp 61–70
Rodriguez MG, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. ICML ’12, pp 313–320
Scott SL (2002) Bayesian methods for hidden markov models: Recursive computing in the 21st century. J Am Stat Assoc 97:337–351
Song D, Meyer DA, Tao D (2015) Efficient latent link recommendation in signed networks. In KDD, pp 1105–1114
Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. KDD ’09, pp 807–816
Tang Y, Xiao X, maximization Y. S. h. i. (2014) Influence Near-optimal time complexity meets practical efficiency. SIGMOD ’14, pp 75–86
Tong G, Wu W, Tang S, Du DZ (2017) Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans Networking 25(1):112–125
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
Wang C, Tang J, Sun J, Han J (2011) Dynamic social influence analysis through time-dependent factor graphs. ASONAM ’11, pp 239–246
Weng J, Lim E-P, Jiang J, He Q (2010) Twitterrank: Finding topic-sensitive influential twitterers. WSDM ’10, pp 261–270
Wood F, Griffiths TL, Ghahramani Z (2006) A non-parametric Bayesian method for inferring hidden causes. UAI ’06, pp 536–543
Zhan Q, Zhang J, Wang S, Yu P, Xie J (2015) Influence maximization across partially aligned heterogenous social networks. In: PAKDD, pp 58–69
Zhang J, Yu PS (2014) Link prediction across heterogeneous social networks: A survey
Zhang J, Yu PS (2015) Integrated anchor and social link predictions across partially aligned social networks. In: IJCAI
Zheng W, Kveton B, Valko M, Vaswani S (2017) Online influence maximization under independent cascade model with semi-bandit feedback. In: NIPS
Zhuang H, Sun Y, Tang J, Zhang J, Sun X (2013) Influence maximization in dynamic social networks. ICDM ’13, pp 1313–1318
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, S., Li, L., Yang, C. et al. Regularized topic-aware latent influence propagation in dynamic relational networks. Geoinformatica 23, 329–352 (2019). https://doi.org/10.1007/s10707-019-00357-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-019-00357-y