Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks
Identifying key information from complex networks is of great practical significance for discovering the community structure. How to effectively use the information of node connections in the network and the attribute information in the attribute network is a major challenge in the current community...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10210566/ |
| _version_ | 1797743433259941888 |
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| author | Weitong Zhang Ronghua Shang Zhiyuan Li Rui Sun Jun Du |
| author_facet | Weitong Zhang Ronghua Shang Zhiyuan Li Rui Sun Jun Du |
| author_sort | Weitong Zhang |
| collection | DOAJ |
| description | Identifying key information from complex networks is of great practical significance for discovering the community structure. How to effectively use the information of node connections in the network and the attribute information in the attribute network is a major challenge in the current community detection problem of attribute networks. A graph convolutional network based on personalized web page ranking algorithm is proposed for community detection in attribute networks in this paper. First, the proposed algorithm uses the strong characteristics of graph convolution algorithm for integrating node topology and attribute information, and combines with personalized web page ranking algorithm to decouple the prediction process and propagation process in the model. In addition, the improved density peak detection method is used to sample the local structure center as a training set for training the algorithm model. Finally, k-means method is used to cluster the node vector representation results and get the community division. The experimental results on 7 datasets with 13 comparison algorithms show that the proposed algorithm has obvious improvement for attribute community detection. |
| first_indexed | 2024-03-12T14:55:24Z |
| format | Article |
| id | doaj.art-3408d6ee71d140659f3fb1220ce9fcea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:55:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3408d6ee71d140659f3fb1220ce9fcea2023-08-14T23:00:24ZengIEEEIEEE Access2169-35362023-01-0111842708428210.1109/ACCESS.2023.330321010210566Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute NetworksWeitong Zhang0https://orcid.org/0000-0003-1625-2870Ronghua Shang1https://orcid.org/0000-0001-9124-696XZhiyuan Li2Rui Sun3Jun Du4College of Information Engineering, Longdong University, Qingyang, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, ChinaCollege of Information Engineering, Longdong University, Qingyang, ChinaCollege of Information Engineering, Longdong University, Qingyang, ChinaIdentifying key information from complex networks is of great practical significance for discovering the community structure. How to effectively use the information of node connections in the network and the attribute information in the attribute network is a major challenge in the current community detection problem of attribute networks. A graph convolutional network based on personalized web page ranking algorithm is proposed for community detection in attribute networks in this paper. First, the proposed algorithm uses the strong characteristics of graph convolution algorithm for integrating node topology and attribute information, and combines with personalized web page ranking algorithm to decouple the prediction process and propagation process in the model. In addition, the improved density peak detection method is used to sample the local structure center as a training set for training the algorithm model. Finally, k-means method is used to cluster the node vector representation results and get the community division. The experimental results on 7 datasets with 13 comparison algorithms show that the proposed algorithm has obvious improvement for attribute community detection.https://ieeexplore.ieee.org/document/10210566/Graph convolutional networkpersonalized web page rankingcommunity detectionattribute networks |
| spellingShingle | Weitong Zhang Ronghua Shang Zhiyuan Li Rui Sun Jun Du Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks IEEE Access Graph convolutional network personalized web page ranking community detection attribute networks |
| title | Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks |
| title_full | Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks |
| title_fullStr | Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks |
| title_full_unstemmed | Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks |
| title_short | Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks |
| title_sort | personalized web page ranking based graph convolutional network for community detection in attribute networks |
| topic | Graph convolutional network personalized web page ranking community detection attribute networks |
| url | https://ieeexplore.ieee.org/document/10210566/ |
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