Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder
Community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. Community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimiz...
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2601 |
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| author | Xiang Gu Qiwei Huang Jie Yang |
| author_facet | Xiang Gu Qiwei Huang Jie Yang |
| author_sort | Xiang Gu |
| collection | DOAJ |
| description | Community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. Community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimizing communication efficiency, supporting resource allocation, and enhancing privacy protection. However, most existing community detection methods focus on non-overlapping communities, usually only considering the topological structure of the network, and often ignoring the attribute information of nodes. To address these problems, this paper proposes a semi-supervised overlapping community detection method based on graph attention autoencoder (CDGAAE). The method consists of three key components: graph attention autoencoder module, modularity optimization enhancement module, and semi-supervised clustering module. First, the graph attention autoencoder module fuses topological information and node attribute information and encodes nodes using a graph attention mechanism. Second, the modularity optimization enhancement module effectively captures the structure of overlapping communities. Finally, the semi-supervised clustering module combines prior information to improve the accuracy of community detection. CDGAAE is comprehensively evaluated on multiple real and synthetic datasets, and experimental results show that CDGAAE outperforms other competing methods. |
| format | Article |
| id | doaj-art-3bee42a7734f4c8abd4784bb01b393a7 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3bee42a7734f4c8abd4784bb01b393a72025-08-20T02:25:08ZengMDPI AGSensors1424-82202025-04-01258260110.3390/s25082601Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention AutoencoderXiang Gu0Qiwei Huang1Jie Yang2School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaSchool of Information Science and Technology, Nantong University, Nantong 226019, ChinaSchool of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaCommunity detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. Community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimizing communication efficiency, supporting resource allocation, and enhancing privacy protection. However, most existing community detection methods focus on non-overlapping communities, usually only considering the topological structure of the network, and often ignoring the attribute information of nodes. To address these problems, this paper proposes a semi-supervised overlapping community detection method based on graph attention autoencoder (CDGAAE). The method consists of three key components: graph attention autoencoder module, modularity optimization enhancement module, and semi-supervised clustering module. First, the graph attention autoencoder module fuses topological information and node attribute information and encodes nodes using a graph attention mechanism. Second, the modularity optimization enhancement module effectively captures the structure of overlapping communities. Finally, the semi-supervised clustering module combines prior information to improve the accuracy of community detection. CDGAAE is comprehensively evaluated on multiple real and synthetic datasets, and experimental results show that CDGAAE outperforms other competing methods.https://www.mdpi.com/1424-8220/25/8/2601graph attention autoencoderoverlapping community detectionsemi-supervised clusteringvehicular social networks |
| spellingShingle | Xiang Gu Qiwei Huang Jie Yang Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder Sensors graph attention autoencoder overlapping community detection semi-supervised clustering vehicular social networks |
| title | Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder |
| title_full | Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder |
| title_fullStr | Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder |
| title_full_unstemmed | Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder |
| title_short | Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder |
| title_sort | overlapping community detection in vehicular social networks based on graph attention autoencoder |
| topic | graph attention autoencoder overlapping community detection semi-supervised clustering vehicular social networks |
| url | https://www.mdpi.com/1424-8220/25/8/2601 |
| work_keys_str_mv | AT xianggu overlappingcommunitydetectioninvehicularsocialnetworksbasedongraphattentionautoencoder AT qiweihuang overlappingcommunitydetectioninvehicularsocialnetworksbasedongraphattentionautoencoder AT jieyang overlappingcommunitydetectioninvehicularsocialnetworksbasedongraphattentionautoencoder |