RI:Small: Relational learning and inference for network models

Purdue University, West Lafayette IN

Investigators

Abstract

Networks are everywhere. Discovering the underlying principles of the networks has great impact on our understanding of complex systems in many scientific, engineering, and social research areas. Nowadays, the availability of network data, such as online social networks from facebook.com or protein-protein interaction data, give researchers unprecedented opportunities to quantitatively study these complex systems. In this project, the PI brings together problems, ideas and techniques from different areas including machine learning, statistics, biology and social sciences, to develop novel computational tools and statistical models for common problems in network inference and learning. The research activities include i) designing nonparametric Bayesian models to discover latent classes from relational data, ii) developing relational Bayesian models, coupled with efficient deterministic approximate inference methods, to predict missing links and node labels, and iii) examining network dynamics at different substructure levels. The developed models, algorithms, and tools for analyzing network data are available to the public via publication and web distribution, disseminating to other machine learning researchers and helping computational biologists and socials scientists analyze massive network data that are being generated with an unprecedented fast speed. The PI incorporates the research results into the graduate-level interdisciplinary courses he teaches and recruits graduate and undergraduate students to conduct research for this project.