A Node Similarity and Community Link Strength-Based Community Discovery Algorithm
Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them....
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8848566 |
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| _version_ | 1832550156896043008 |
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| author | Haijuan Yang Jianjun Cheng Zeyi Yang Handong Zhang Wenbo Zhang Ke Yang Xiaoyun Chen |
| author_facet | Haijuan Yang Jianjun Cheng Zeyi Yang Handong Zhang Wenbo Zhang Ke Yang Xiaoyun Chen |
| author_sort | Haijuan Yang |
| collection | DOAJ |
| description | Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms. |
| format | Article |
| id | doaj-art-9ed623954638489db5888e91b2e3417d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9ed623954638489db5888e91b2e3417d2025-02-03T06:07:37ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88485668848566A Node Similarity and Community Link Strength-Based Community Discovery AlgorithmHaijuan Yang0Jianjun Cheng1Zeyi Yang2Handong Zhang3Wenbo Zhang4Ke Yang5Xiaoyun Chen6School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaCommunity structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms.http://dx.doi.org/10.1155/2021/8848566 |
| spellingShingle | Haijuan Yang Jianjun Cheng Zeyi Yang Handong Zhang Wenbo Zhang Ke Yang Xiaoyun Chen A Node Similarity and Community Link Strength-Based Community Discovery Algorithm Complexity |
| title | A Node Similarity and Community Link Strength-Based Community Discovery Algorithm |
| title_full | A Node Similarity and Community Link Strength-Based Community Discovery Algorithm |
| title_fullStr | A Node Similarity and Community Link Strength-Based Community Discovery Algorithm |
| title_full_unstemmed | A Node Similarity and Community Link Strength-Based Community Discovery Algorithm |
| title_short | A Node Similarity and Community Link Strength-Based Community Discovery Algorithm |
| title_sort | node similarity and community link strength based community discovery algorithm |
| url | http://dx.doi.org/10.1155/2021/8848566 |
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