Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process

Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of events like "post" or "repost" associated with each node, and (2) the interactions between nodes encompass multiple distinct patterns which in turn affect the diffusion patterns. For instance, social interactions seldom develop on a single channel, and multiple relationships can bind pairs of people due to their various common interests. Most previous work considers only one of these two challenges which is apparently unrealistic. In this paper, we study the problem of \emph{inferring multiplex network} in social networks. We propose the Multiplex Diffusion Model (MDM) which incorporates the multivariate marked Hawkes process and topic model to infer the multiplex structure of social network. A MCMC based algorithm is developed to infer the latent multiplex structure and to estimate the node-related parameters. We evaluate our model based on both synthetic and real-world datasets. The results show that our model is more effective in terms of uncovering the multiplex network structure.

[1]  Le Song,et al.  Uncover Topic-Sensitive Information Diffusion Networks , 2013, AISTATS.

[2]  Kathleen M. Carley,et al.  Exact and approximate EM estimation of mutually exciting hawkes processes , 2013 .

[3]  Jennifer Wortman,et al.  Viral Marketing and the Diffusion of Trends on Social Networks , 2008 .

[4]  Zhoujun Li,et al.  Diabetes-Associated Factors as Predictors of Nursing Home Admission and Costs in the Elderly Across Europe. , 2017, Journal of the American Medical Directors Association.

[5]  Bernhard Schölkopf,et al.  Uncovering the Temporal Dynamics of Diffusion Networks , 2011, ICML.

[6]  G. Ronning.  Maximum likelihood estimation of dirichlet distributions , 1989 .

[7]  Le Song,et al.  Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes , 2013, AISTATS.

[8]  T. Taimre,et al.  Hawkes Processes , 2015, 1507.02822.

[9]  Nicola Barbieri,et al.  Cascade-based community detection , 2013, WSDM.

[10]  David M Blei,et al.  Efficient discovery of overlapping communities in massive networks , 2013, Proceedings of the National Academy of Sciences.

[11]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[12]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[13]  Jure Leskovec,et al.  Patterns of Influence in a Recommendation Network , 2006, PAKDD.

[14]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[15]  Thomas Josef Liniger,et al.  Multivariate Hawkes processes , 2009 .

[16]  Nicola Barbieri,et al.  Who to follow and why: link prediction with explanations , 2014, KDD.

[17]  Scott W. Linderman,et al.  Discovering Latent Network Structure in Point Process Data , 2014, ICML.

[18]  Shuang-Hong Yang,et al.  Mixture of Mutually Exciting Processes for Viral Diffusion , 2013, ICML.

[19]  Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.

[20]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockstructures , 2001 .

[21]  Steffen Bickel,et al.  Unsupervised prediction of citation influences , 2007, ICML '07.

[22]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[23]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[24]  James R. Foulds,et al.  HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades , 2015, ICML.

[25]  Sujay Sanghavi,et al.  Learning the graph of epidemic cascades , 2012, SIGMETRICS '12.

[26]  Shuang Li,et al.  COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution , 2015, NIPS.

[27]  Le Song,et al.  Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams , 2015, KDD.