Soft Partitioning in Networks via Bayesian Non-negative Matrix Factorization
نویسندگان
چکیده
Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilizes the Bayesian non-negative matrix factorization (NMF) model to extract overlapping modules from a network. The scheme has the advantage of computational efficiency, soft community membership and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection.
منابع مشابه
Overlapping community detection using Bayesian non-negative matrix factorization.
Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, and an intuitive foundation. ...
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