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Xiaoyun Chen
Lanzhou University

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Community Detection Algorithm Based on Neighbor Similarity Jianjun Cheng; Hong Xu; Mahmud Gaybullaev; Mingwei Leng; Xiaoyun Chen
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 8: August 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Many complex networks have displayed the community structures, and the detection of community structure can give insights into the structural and functional information of these complex networks. In this paper, we proposed a neighbor similarity based new algorithm for community structure detection, in which we only consider the similarities between a node and its unclassified neighbors in the breadth-first traversal order, without considering other nodes’ influences; we take this node as a father node and its neighbors as the children nodes, to find out those children nodes which should belong in the same community with their father node. Then these children nodes are processed in the same way as their father node recursively, until the termination condition is reached. The most prominent property of our algorithm is that it has near liner time complexity, and furthermore it is a deterministic algorithm. We have tested our algorithm on several real networks, compared with some other algorithms, and the results have manifested that our algorithm outperforms the previous algorithms significantly. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3064 
Hierarchical Agglomeration Community Detection Algorithm via Community Similarity Measures Mingwei Leng; Jinjin Wang; Pengfei Wang; Xiaoyun Chen
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 6: October 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Community detection is an important method for analyzing the community structure of real-world networks. Most of the hierarchical agglomeration community detection algorithms are the variations of NM algorithm. In this contribution, we present a new hierarchical agglomeration community detection algorithm, called Community Merging via Community Similarity Measures (CMCSM). The proposed algorithm encompasses three components. It first repeatedly joins communities by using single-node community measure and combination rule. Then it adjusts a few nodes by SHARC which is an advanced label propagation algorithm. Finally, it merges communities by using community similarity measure. Four of most important features of CMCSM are that (1) it requires only a single parameter which is the number of community count, (2) it can prevent single-node communities and monster communities from being created, (3) it is well suited for a wide range of networks and (4) its computation is not expensive. The algorithm CMCSM is demonstrated with real-world and artificial networks, the experiment shows that CMCSM has a more efficient and accurate result of community detection compared with some hierarchical algorithms recently proposed. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1430
Co-Authors Hong Xu Jianjun Cheng Jinjin Wang Mahmud Gaybullaev Mingwei Leng Pengfei Wang