Community-based relational Markov networks in complex networks

Abstract

Relational Markov networks (RMNs) are a joint probabilistic model for an entire collection of related entities. The model is able to mine relational data effectively by integrating information from content attributes of individual entities as well as the links among them, yet the prediction accuracy is greatly affected by the definition of the relational clique templates. Maximum likelihood estimation (MLE) is used to estimate the model parameters, but this can be quite costly because multiple rounds of approximate inference are required over the entire dataset. In this paper, we propose constructing RMNs basing on the community structures of complex networks, and present a discriminative maximum pseudolikelihood estimation (DMPLE) approach for training RMNs. Experiments on the collective classification and link prediction tasks on some real-world datasets show that our approaches perform well in terms of accuracy and efficiency. © 2011 Springer-Verlag.