GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set.
Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convolutional network (GCN) i...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327022 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850116368140075008 |
|---|---|
| author | Bing Guo Liping Deng Tao Lian |
| author_facet | Bing Guo Liping Deng Tao Lian |
| author_sort | Bing Guo |
| collection | DOAJ |
| description | Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convolutional network (GCN) is recently exploited to realize unsupervised community detection. However, the results are highly dependent on initial structure centers. Moreover, a shallow GCN cannot effectively propagate a limited amount of label information to the entire graph, since the graph convolution is a localized filter. In this paper, we develop a GCN-based unsupervised community detection method with structure center Refinement and pseudo-labeled set Expansion (RE-GCN), considering both the network topology and node attributes. To reduce the adverse effect of inappropriate structure centers, we iteratively refine them by alternating between two steps: obtaining a temporary graph partition by a GCN trained with the current structure centers; updating each structure center to the node with the highest structure importance in the corresponding induced subgraph. To improve the label propagation ability of shallow GCN, we expand the pseudo-labeled set by selecting a few nodes whose affiliation strengths to a community are similar to that of its structure center. The final GCN is trained with the expanded pseudo-labeled set to realize community detection. Extensive experiments demonstrate the effectiveness of the proposed approach on both attributed and non-attributed networks. The refinement process yields a set of more representative structure centers, and the community detection performance of GCN improves as the number of pseudo-labeled nodes increase. |
| format | Article |
| id | doaj-art-499db400dd3e4ff589a3deca5d4659fe |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-499db400dd3e4ff589a3deca5d4659fe2025-08-20T02:36:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032702210.1371/journal.pone.0327022GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set.Bing GuoLiping DengTao LianCommunity detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convolutional network (GCN) is recently exploited to realize unsupervised community detection. However, the results are highly dependent on initial structure centers. Moreover, a shallow GCN cannot effectively propagate a limited amount of label information to the entire graph, since the graph convolution is a localized filter. In this paper, we develop a GCN-based unsupervised community detection method with structure center Refinement and pseudo-labeled set Expansion (RE-GCN), considering both the network topology and node attributes. To reduce the adverse effect of inappropriate structure centers, we iteratively refine them by alternating between two steps: obtaining a temporary graph partition by a GCN trained with the current structure centers; updating each structure center to the node with the highest structure importance in the corresponding induced subgraph. To improve the label propagation ability of shallow GCN, we expand the pseudo-labeled set by selecting a few nodes whose affiliation strengths to a community are similar to that of its structure center. The final GCN is trained with the expanded pseudo-labeled set to realize community detection. Extensive experiments demonstrate the effectiveness of the proposed approach on both attributed and non-attributed networks. The refinement process yields a set of more representative structure centers, and the community detection performance of GCN improves as the number of pseudo-labeled nodes increase.https://doi.org/10.1371/journal.pone.0327022 |
| spellingShingle | Bing Guo Liping Deng Tao Lian GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. PLoS ONE |
| title | GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. |
| title_full | GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. |
| title_fullStr | GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. |
| title_full_unstemmed | GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. |
| title_short | GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. |
| title_sort | gcn based unsupervised community detection with refined structure centers and expanded pseudo labeled set |
| url | https://doi.org/10.1371/journal.pone.0327022 |
| work_keys_str_mv | AT bingguo gcnbasedunsupervisedcommunitydetectionwithrefinedstructurecentersandexpandedpseudolabeledset AT lipingdeng gcnbasedunsupervisedcommunitydetectionwithrefinedstructurecentersandexpandedpseudolabeledset AT taolian gcnbasedunsupervisedcommunitydetectionwithrefinedstructurecentersandexpandedpseudolabeledset |