Learning a proximity measure to complete a community

Maximilien Danisch, Jean-Loup Guillaume, Benedicte Le Grand
2014 2014 International Conference on Data Science and Advanced Analytics (DSAA)  
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 parameterized proximity measure which, given a set of nodes belonging to a community, learns the optimal parameters and identifies the other nodes of
more » ... s 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.
doi:10.1109/dsaa.2014.7058057 dblp:conf/dsaa/DanischGG14 fatcat:px6es7cazjboxjqirl2apkos7i