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Locally Boosted Graph Aggregation for Community Detection
[article]
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
arXiv:1405.3210v1
fatcat:ixdj2y7i5zeuboy2csqszrr3ca