A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks
Community structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few comm...
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doaj-a0dfd0788e074e929232d533b94aae962021-03-29T23:24:16ZengIEEEIEEE Access2169-35362019-01-01712158612159810.1109/ACCESS.2019.29375808821353A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social NetworksXiaohui Pan0https://orcid.org/0000-0002-8105-8380Guiqiong Xu1Bing Wang2Tao Zhang3https://orcid.org/0000-0002-8440-9924School of Management, Shanghai University, Shanghai, ChinaSchool of Management, Shanghai University, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaCommunity structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few community detection algorithms using node similarity. In most real-world networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties. The higher the clustering coefficient of a node, the more aggregative the neighboring nodes are. In this paper, we propose an adjacent node similarity optimization combination connectivity algorithm (ASOCCA) for accurate community detection. ASOCCA utilizes the local similarity measure based on clustering coefficient to identify the closest neighbors of each node, then obtains several sets of connected components by combining different pairs of nodes, and finally forms initial communities. In addition, the community merging strategy is applied to further optimize the community structure. To evaluate the performance of the proposed algorithm, six real-world networks and two LFR networks with diverse network size are used to compare ASOCCA with five state-of-the-art community detection algorithms. The experimental results show that ASOCCA achieves better detection accuracy than several existing algorithms.https://ieeexplore.ieee.org/document/8821353/Social networkscommunity detectionlocal similaritymerging strategy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaohui Pan Guiqiong Xu Bing Wang Tao Zhang |
spellingShingle |
Xiaohui Pan Guiqiong Xu Bing Wang Tao Zhang A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks IEEE Access Social networks community detection local similarity merging strategy |
author_facet |
Xiaohui Pan Guiqiong Xu Bing Wang Tao Zhang |
author_sort |
Xiaohui Pan |
title |
A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks |
title_short |
A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks |
title_full |
A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks |
title_fullStr |
A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks |
title_full_unstemmed |
A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks |
title_sort |
novel community detection algorithm based on local similarity of clustering coefficient in social networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Community structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few community detection algorithms using node similarity. In most real-world networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties. The higher the clustering coefficient of a node, the more aggregative the neighboring nodes are. In this paper, we propose an adjacent node similarity optimization combination connectivity algorithm (ASOCCA) for accurate community detection. ASOCCA utilizes the local similarity measure based on clustering coefficient to identify the closest neighbors of each node, then obtains several sets of connected components by combining different pairs of nodes, and finally forms initial communities. In addition, the community merging strategy is applied to further optimize the community structure. To evaluate the performance of the proposed algorithm, six real-world networks and two LFR networks with diverse network size are used to compare ASOCCA with five state-of-the-art community detection algorithms. The experimental results show that ASOCCA achieves better detection accuracy than several existing algorithms. |
topic |
Social networks community detection local similarity merging strategy |
url |
https://ieeexplore.ieee.org/document/8821353/ |
work_keys_str_mv |
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