Deep Generative Models for Relational Data with Side Information
This work addresses the challenge of analyzing relational data with side information for researchers and practitioners in network analysis, offering an incremental improvement through a unified probabilistic framework.
The authors tackled the problem of overlapping community discovery and link prediction in relational graphs by introducing a deep generative model that incorporates node attributes, achieving superior link prediction performance on complex networks and better interpretability of latent features.
We present a probabilistic framework for overlapping community discovery and link prediction for relational data, given as a graph. The proposed framework has: (1) a deep architecture which enables us to infer multiple layers of latent features/communities for each node, providing superior link prediction performance on more complex networks and better interpretability of the latent features; and (2) a regression model which allows directly conditioning the node latent features on the side information available in form of node attributes. Our framework handles both (1) and (2) via a clean, unified model, which enjoys full local conjugacy via data augmentation, and facilitates efficient inference via closed form Gibbs sampling. Moreover, inference cost scales in the number of edges which is attractive for massive but sparse networks. Our framework is also easily extendable to model weighted networks with count-valued edges. We compare with various state-of-the-art methods and report results, both quantitative and qualitative, on several benchmark data sets.