A Boosting Approach to Learning Graph Representations

TL;DR

This paper introduces a boosting-inspired framework for learning robust graph representations from noisy, multisource data, enhancing community detection by integrating local edge quality measurements into a global similarity metric.

Contribution

It proposes a novel boosting-based method to combine weak evidence into a comprehensive graph representation, with empirical validation on synthetic and real datasets.

Findings

Improved community detection performance

Effective integration of local edge quality

Robustness to noisy, multisource data

Abstract

Learning the right graph representation from noisy, multisource data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here the objective is to incorporate the partial information each data source gives us in a way that captures the true underlying relationships. To address this challenge, we present a general, boosting-inspired framework for combining weak evidence of entity associations into a robust similarity metric. We explore the extent to which different quality measurements yield graph representations that are suitable for community detection. We then present empirical results on both synthetic and real datasets demonstrating the utility of this framework. Our framework leads to suitable global graph representations from quality measurements local to each edge. Finally, we discuss future extensions and theoretical considerations of learning useful graph representations from weak feedback in general application settings.