Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
This work addresses the crucial issue of missing data in social network analysis, which is often overlooked, by providing a method to recover such data, though it is incremental as it builds on existing matrix factorization and generative modeling approaches.
The paper tackles the problem of missing data in social networks by developing a probabilistic generative model, DiffStru, to jointly infer unobserved diffusion activities and hidden network structures from partially observed data, achieving successful detection of invisible social behaviors, link prediction, and latent feature identification on synthetic and real-world datasets like Twitter and Memtracker.
Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks does not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. To jointly discover omitted diffusion activities and hidden network structures, we develop a probabilistic generative model called "DiffStru." The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled with low-dimensional latent factors. Besides inferring unseen data, latent factors such as community detection may also aid in network classification problems. We tested different missing data scenarios on simulated independent cascades over LFR networks and real datasets, including Twitter and Memtracker. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.