Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process
This addresses the challenge of hidden and complex social network diffusion for researchers in network analysis, though it is incremental as it builds on existing Hawkes process and topic modeling techniques.
The paper tackles the problem of inferring multiplex network structure in social networks from observed diffusion events, proposing a model that combines multivariate marked Hawkes processes and topic models, with results showing improved effectiveness in uncovering the multiplex structure compared to baselines.
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.