Bloom: A stochastic growth-based fast method of community detection in networks |
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| Authors: | Phillip Schumm Caterina Scoglio |
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| Institution: | Sunflower Networking Group and K-State Epicenter, Electrical and Computer Engineering Department, Kansas State University, Manhattan, KS, USA;National Research University of Information Technologies, Mechanics and Optics, Birzhevaya Line 4, 199034 Saint-Petersburg, Russia;Dpto. Cómputo Científico y Estadística, Universidad Simón Bolívar, Caracas, Venezuela;AGH University of Sciences and Technology, Faculty of Computer Science, Electronics and Telecommunication, Department of Computer Science, al. A Mickiewicza 30, 30-059 Krakow, Poland;Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, United States;Institute of Chemical Physics, Vilnius University, Saul?tekio al. 9, bldg. III, Vilnius 10222, Lithuania;Lehrstuhl für Systemsimulation, Friedrich-Alexander Universität Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany |
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| Abstract: | Networks are characterized by a variety of topological features and dynamics. Classifying nodes into communities, community structure, is important when exploring networks. This paper explores the community detection metric called modularity. The theoretical definitions of modularity are connected with intuitive insights into the compositions of communities. Local modularity costs/benefits are explored and an efficient stochastic algorithm, Bloom, is introduced, based on growing communities using local improvement measures. Three extensions of Bloom are presented that build upon the basic version. A numerical analysis compares Bloom with the popular fast-greedy algorithm and demonstrates the successful performance of the three modifications of Bloom. |
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