Friend Recommendation Based on Multi-Social Graph Convolutional Network
Friend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks. However, most of the existing friend recommendation methods use user attributes or a single social network, while rarely integrating multiple social relationships...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9019700/ |
| _version_ | 1818933121827995648 |
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| author | Liang Chen Yuanzhen Xie Zibin Zheng Huayou Zheng Jingdun Xie |
| author_facet | Liang Chen Yuanzhen Xie Zibin Zheng Huayou Zheng Jingdun Xie |
| author_sort | Liang Chen |
| collection | DOAJ |
| description | Friend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks. However, most of the existing friend recommendation methods use user attributes or a single social network, while rarely integrating multiple social relationships to enhance the representation. This paper focuses on integrating various social relationships to guide the representation learning, and further generating personalized friend recommendations. We design an end-to-end framework based on multiple social networks to learn the potential features of users and construct a friend recommendation model named Multi-Social Graph Convolutional Network (MSGCN). It learns the features of higher-order neighbors from multiple social networks to enrich the representation of the target user based on the improved graph convolution neural network. In particular, some graph fusion strategies by adjusting and fusing the Laplace matrix of the graph are designed to integrate social relationships. Finally, we use Bayesian theory to transform friend recommendation into a sorting problem for personalized recommendation. The experimental results show that the proposed model outperforms the state-of-the-art methods. |
| first_indexed | 2024-12-20T04:43:21Z |
| format | Article |
| id | doaj.art-08c4869490ad4b0f84e139dc1d4ab2bf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:43:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-08c4869490ad4b0f84e139dc1d4ab2bf2022-12-21T19:53:04ZengIEEEIEEE Access2169-35362020-01-018436184362910.1109/ACCESS.2020.29774079019700Friend Recommendation Based on Multi-Social Graph Convolutional NetworkLiang Chen0Yuanzhen Xie1https://orcid.org/0000-0002-8010-3434Zibin Zheng2https://orcid.org/0000-0001-7872-7718Huayou Zheng3Jingdun Xie4School of Data and Computer Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Data and Computer Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Data and Computer Science, Sun Yat-sen University, Guangzhou, ChinaGuangdong Provincial Public Security Department, Guangzhou, ChinaDepartment of Anesthesiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaFriend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks. However, most of the existing friend recommendation methods use user attributes or a single social network, while rarely integrating multiple social relationships to enhance the representation. This paper focuses on integrating various social relationships to guide the representation learning, and further generating personalized friend recommendations. We design an end-to-end framework based on multiple social networks to learn the potential features of users and construct a friend recommendation model named Multi-Social Graph Convolutional Network (MSGCN). It learns the features of higher-order neighbors from multiple social networks to enrich the representation of the target user based on the improved graph convolution neural network. In particular, some graph fusion strategies by adjusting and fusing the Laplace matrix of the graph are designed to integrate social relationships. Finally, we use Bayesian theory to transform friend recommendation into a sorting problem for personalized recommendation. The experimental results show that the proposed model outperforms the state-of-the-art methods.https://ieeexplore.ieee.org/document/9019700/Friend recommendationmulti-social graph convolutional networksocial network |
| spellingShingle | Liang Chen Yuanzhen Xie Zibin Zheng Huayou Zheng Jingdun Xie Friend Recommendation Based on Multi-Social Graph Convolutional Network IEEE Access Friend recommendation multi-social graph convolutional network social network |
| title | Friend Recommendation Based on Multi-Social Graph Convolutional Network |
| title_full | Friend Recommendation Based on Multi-Social Graph Convolutional Network |
| title_fullStr | Friend Recommendation Based on Multi-Social Graph Convolutional Network |
| title_full_unstemmed | Friend Recommendation Based on Multi-Social Graph Convolutional Network |
| title_short | Friend Recommendation Based on Multi-Social Graph Convolutional Network |
| title_sort | friend recommendation based on multi social graph convolutional network |
| topic | Friend recommendation multi-social graph convolutional network social network |
| url | https://ieeexplore.ieee.org/document/9019700/ |
| work_keys_str_mv | AT liangchen friendrecommendationbasedonmultisocialgraphconvolutionalnetwork AT yuanzhenxie friendrecommendationbasedonmultisocialgraphconvolutionalnetwork AT zibinzheng friendrecommendationbasedonmultisocialgraphconvolutionalnetwork AT huayouzheng friendrecommendationbasedonmultisocialgraphconvolutionalnetwork AT jingdunxie friendrecommendationbasedonmultisocialgraphconvolutionalnetwork |