Locally Boosted Graph Aggregation for Community Detection [article]

Jeremy Kun, Rajmonda Caceres, Kevin Carter
2014 arXiv   pre-print
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
more » ... ous 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.
arXiv:1405.3210v1 fatcat:ixdj2y7i5zeuboy2csqszrr3ca