| A weight’s agglomerative method for detecting communities in weighted networks based on weight’s similarity |
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| 引用本文： | 沈毅.A weight’s agglomerative method for detecting communities in weighted networks based on weight’s similarity[J].中国物理 B,2011,20(4):40511-040511. |
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| 作者姓名： | 沈毅 |
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| 作者单位： | College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China |
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| 基金项目： | Project supported by the Fundamental Research Funds for the Central Universities (Grant Nos. KYZ200916, KYZ200919 and KYZ201005) and the Youth Sci-Tech Innovation Fund, Nanjing Agricultural University (Grant No. KJ2010024). |
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| 摘 要： | This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge’s weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes’ functions effectively. In order to detect the community structure efficiently,a threshold coefficient κ to evaluate the equivalence of edges’ weights and a new weighted modularity based on the weight’s similarity are proposed. Then,constructing the weighted matrix and using the agglomerative mechanism,it presents a weight’s agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes,the algorithm can detect the community structure in time O(n 2 log n 2 ). Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore,with the change of κ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.
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| 关 键 词： | complex networks weight’s similarity community structure weight’s agglomerative method |
| 收稿时间： | 2010-08-28 |
A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity |
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| Shen Yi.A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity[J].Chinese Physics B,2011,20(4):40511-040511. |
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| Authors: | Shen Yi |
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| Institution: | College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China |
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| Abstract: | This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge's weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes' functions effectively. In order to detect the community structure efficiently, a threshold coefficient κ to evaluate the equivalence of edges' weights and a new weighted modularity based on the weight's similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight's agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O(n2log2n). Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of κ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups. |
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| Keywords: | complex networks weight's similarity community structure weight's agglomerative method |
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