A Boosting Approach to Learning Graph Representations
This work addresses the challenge of relational learning for researchers and practitioners in graph analysis, though it appears incremental as it builds on existing boosting methods for graph representation.
The paper tackles the problem of learning graph representations from noisy, multisource data by developing a boosting-inspired framework to combine weak evidence into a robust similarity metric, with empirical results on synthetic and real datasets demonstrating its utility for community detection.
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.