Abstract
This paper introduces an algorithm for network community detection called DEEN, (Delete Edges and Expand Nodes) consisting of two simple steps. First edges of the graph estimated to connect different clusters are detected and removed, next the resulting graph is used for generating communities by expanding seed nodes.
DEEN, uses as parameters the minimum and maximum allowed size of a cluster, and a resolution parameter whose value influences the number of removed edges. Application of DEEN, to the budding yeast protein network for detecting functional protein complexes indicates its capability to identify clusters containing proteins with the same functional category, improving on MCL, a popular state-of-the-art method for functional protein complex detection. Moreover, application of DEEN, to two popular benchmark networks results in the detection of accurate communities, substantiating the effectiveness of the proposed method in diverse domains.
Keywords
- Community detection
- protein interaction networks
- graph sparsification
- heuristic search
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Jancura, P., Mavroeidis, D., Marchiori, E. (2012). DEEN: A Simple and Fast Algorithm for Network Community Detection. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_13
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DOI: https://doi.org/10.1007/978-3-642-35686-5_13
Publisher Name: Springer, Berlin, Heidelberg
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