Locally Boosted Graph Aggregation for Community Detection

TL;DR

This paper introduces a boosting-inspired framework for aggregating multiple noisy graph sources into a robust representation that improves community detection, validated through empirical results and theoretical convergence analysis.

Contribution

It proposes a novel, general method for combining weak evidence from multiple data sources into a strong, noise-resistant graph representation for community detection.

Findings

Effective in noisy, multi-source datasets

Improves community detection accuracy

Proven convergence in ideal conditions

Abstract

Learning the right graph representation from noisy, multi-source 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. Building on previous work, we explore the extent to which different local quality measurements yield graph representations that are suitable for community detection. We present empirical results on a variety of datasets demonstrating the utility of this framework, especially with respect to real datasets where noise and scale present serious challenges. Finally, we prove a convergence theorem in an ideal setting and outline future research into other application domains.