Deep generative models for relational data with side information

Published

Conference Paper

© Copyright 2017 by the author(s). 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.

Duke Authors

Cited Authors

  • Hu, C; Rai, P; Carin, L

Published Date

  • January 1, 2017

Published In

  • 34th International Conference on Machine Learning, Icml 2017

Volume / Issue

  • 4 /

Start / End Page

  • 2492 - 2502

International Standard Book Number 13 (ISBN-13)

  • 9781510855144

Citation Source

  • Scopus