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| Formalising and Detecting Community Structures in Real World Complex Networks | |
| 作者姓名： | KUMAR Pawan DOHARE Ravins |
| 作者单位： | School of Sciences;Centre for Interdisciplinary Research in Basic Sciences |
| 基金项目： | supported by the Science and Engineering Research Board,D.S.T.,Govt.of India,India under Grant No.EEQ/2016/000509。 |
| 摘 要： | Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas. |
| 关 键 词： | Algorithm HUB local maximal p-community OUTLIER p-community strength of a subgraph |
Formalising and Detecting Community Structures in Real World Complex Networks |
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| KUMAR Pawan,DOHARE Ravins.Formalising and Detecting Community Structures in Real World Complex Networks[J].Journal of Systems Science and Complexity,2021,34(1):180-205. | |
| Authors: | Kumar Pawan Dohare Ravins |
| Institution: | 1.School of Sciences, Indira Gandhi National Open University, New Delhi, 110068, India ;2.Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India ; |
| Abstract: | Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas. |
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