Cargando…
Overlapping Community Detection Based on Membership Degree Propagation
A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping communi...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824673/ https://www.ncbi.nlm.nih.gov/pubmed/33374305 http://dx.doi.org/10.3390/e23010015 |
_version_ | 1783640135124910080 |
---|---|
author | Gao, Rui Li, Shoufeng Shi, Xiaohu Liang, Yanchun Xu, Dong |
author_facet | Gao, Rui Li, Shoufeng Shi, Xiaohu Liang, Yanchun Xu, Dong |
author_sort | Gao, Rui |
collection | PubMed |
description | A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general. |
format | Online Article Text |
id | pubmed-7824673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78246732021-02-24 Overlapping Community Detection Based on Membership Degree Propagation Gao, Rui Li, Shoufeng Shi, Xiaohu Liang, Yanchun Xu, Dong Entropy (Basel) Article A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general. MDPI 2020-12-24 /pmc/articles/PMC7824673/ /pubmed/33374305 http://dx.doi.org/10.3390/e23010015 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Rui Li, Shoufeng Shi, Xiaohu Liang, Yanchun Xu, Dong Overlapping Community Detection Based on Membership Degree Propagation |
title | Overlapping Community Detection Based on Membership Degree Propagation |
title_full | Overlapping Community Detection Based on Membership Degree Propagation |
title_fullStr | Overlapping Community Detection Based on Membership Degree Propagation |
title_full_unstemmed | Overlapping Community Detection Based on Membership Degree Propagation |
title_short | Overlapping Community Detection Based on Membership Degree Propagation |
title_sort | overlapping community detection based on membership degree propagation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824673/ https://www.ncbi.nlm.nih.gov/pubmed/33374305 http://dx.doi.org/10.3390/e23010015 |
work_keys_str_mv | AT gaorui overlappingcommunitydetectionbasedonmembershipdegreepropagation AT lishoufeng overlappingcommunitydetectionbasedonmembershipdegreepropagation AT shixiaohu overlappingcommunitydetectionbasedonmembershipdegreepropagation AT liangyanchun overlappingcommunitydetectionbasedonmembershipdegreepropagation AT xudong overlappingcommunitydetectionbasedonmembershipdegreepropagation |