Learning a proximity measure to complete a community

Abstract : In large-scale online complex networks (Wikipedia, Facebook, Twitter, etc.) finding nodes related to a specific topic is a strategic research subject. This article focuses on two central notions in this context: communities (groups of highly connected nodes) and proximity measures (indicating whether nodes are topologically close). We propose a parametrized proximity measure which, given a set of nodes belonging to a community, learns the optimal parameters and identifies the other nodes of this community, called multi-ego-centered community as it is centered on a set of nodes. We validate our results on a large dataset of categorized Wikipedia pages and on benchmarks, we also show that our approach performs better than existing ones. Our main contributions are (i) a new ergonomic parametrized proximity measure, (ii) the automatic tuning of the proximity's parameters and (iii) the unsupervised detection of community boundaries.
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Maximilien Danisch, Jean-Loup Guillaume, Bénédicte Le Grand. Learning a proximity measure to complete a community. 2014 International Conference on Data Science and Advanced Analytics (DSAA2014), Oct 2014, Shanghai, China. pp.90-96, ⟨10.1109/DSAA.2014.7058057⟩. ⟨hal-01208519⟩

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