期刊
PHYSICAL REVIEW E
卷 83, 期 6, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.83.066114
关键词
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资金
- UK Research Council [EP/I011587/1]
- Microsoft Research
- EPSRC [EP/I011587/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I011587/1] Funding Source: researchfish
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. 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.
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