Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process

摘要

Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.