An efficient algorithm for community mining with overlap in social networks |
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| Affiliation: | 1. MARS (Modeling of Automated Reasoning Systems) Research Group, FSM/University of Monastir, Tunisia;2. Insitute of Computer Science and Telecom (ISITCom), University of Sousse, Tunisia;1. Department of Business and Entrepreneurial Management, Kainan University, 1, Kainan Road, Luchu Shiang, Taoyuan 33857, Taiwan;2. Graduate Institute of Management Science, National Chiao Tung University, 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan;3. Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, 151, University Road, San Shia 237, Taiwan;1. Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil;2. Department of Production Engineering & Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil;1. University of Pinar del Rio “Hermanos Saiz Montes de Oca”, Road Marti, No. 272, Pinar del Rio, Cuba;2. University “Pablo de Olavide”, Road Utrera, km 1, 41013 Sevilla, Spain;1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, PR China;2. School of Computer Science, Shaanxi Normal University, Xi’an, PR China;1. Department of Computing Languages and Systems, University of Sevilla, ETSII, Avda. de la Reina Mercedes s/n, 41012 Sevilla, Spain |
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| Abstract: | Detecting communities in social networks represents a significant task in understanding the structures and functions of networks. Several methods are developed to detect disjoint partitions. However, in real graphs vertices are often shared between communities, hence the notion of overlap. The study of this case has attracted, recently, an increasing attention and many algorithms have been designed to solve it. In this paper, we propose an overlapping communities detecting algorithm called DOCNet (Detecting overlapping communities in Networks). The main strategy of this algorithm is to find an initial core and add suitable nodes to expand it until a stopping criterion is met. Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that DOCNet is efficient and highly reliable for detecting overlapping groups, compared with four newly known proposals. |
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| Keywords: | Social networks Communities Overlap Objective function Fuzzy membership degree |
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