Temporal Dual-Attributed Network Generation Oriented Community Detection Model

Community detection is a crucial task in the research field of network analysis. However, this task recently has become challenging due to the explosion of network in terms of the scale and the side information, e.g., temporal information and attribute information. Here we propose
PGMTAN
—a probabilistic generative model for overlapping community detection on temporal dual-attributed networks.
PGMTAN
aims to characterize four generation processes: 1) generation of occurrence of the links, 2) generation of node-community memberships via assortative attributes, 3) generation of generative attributes, and 4) generation of evolutionary dynamics of community structure. Particularly, we adopt a hidden Markov chain model to capture the network's dynamics on the evolution of community structure over time. Moreover, we seek to optimize a lower-bound of likelihood function to accelerate the model's parameter estimation. We carry out extensive experiments on several real-world and synthetic networks to test
PGMTAN
's performance and the results substantiate that it can outperform multiple baselines and give us promising performance in terms of detection accuracy and convergence.