An End-to-End Community Detection Model:
Integrating LDA into Markov Random Field via Factor Graph
Abstract
Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network
topology while ignore the semantic attributes. A
straightforward way to combine the two types of
information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then
take this result as a prior to define the MRF model. In this way, however, the parameters of the two
models cannot be adjusted by each other, preventing it from really realizing the complementation of
the advantages of the two. This paper integrates
LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly.
However, LDA is a directed graphic model whereas
MRF is undirected, making their integration a challenge. To handle this problem, we first transform
LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two
models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage
of the strength of both LDA and MRF. Empirical
results show that our approach compares favorably
with the state-of-the-art methods