Social Network Embedding Method Combining Node Attributes and Loop-Free Path
Network embedding’s goal is to learn the low-dimensional node feature representation in the network. The learned features are used in various network analysis tasks, such as node classification, link prediction, community detection and recommendation, etc. The existing network embedding methods do n...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-11-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2104075.pdf |
| _version_ | 1811308328089812992 |
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| author | WANG Benyu, GU Yijun, PENG Shufan |
| author_facet | WANG Benyu, GU Yijun, PENG Shufan |
| author_sort | WANG Benyu, GU Yijun, PENG Shufan |
| collection | DOAJ |
| description | Network embedding’s goal is to learn the low-dimensional node feature representation in the network. The learned features are used in various network analysis tasks, such as node classification, link prediction, community detection and recommendation, etc. The existing network embedding methods do not make full use of high-order structure information in social networks. Moreover, the correlation between structure information and node attribute information is not considered. The effect of these methods applied in the social network is not ideal. A social network embedding method combining loop-free path and attributes network embedding (LFNE) is proposed to solve these problems. The high-order structural similarity of nodes is calculated first based on the loop-free path between nodes to eliminate the influence of loop path and large-degree nodes on node structure similarity. This algorithm makes the network embedding method better integrate the high-order social network structure information. Then the node attributes similarity is calculated by combining the loop-free path similarity measurement index between nodes, and the correlation between social network structure information and attribute information is fully utilized to eliminate the noise in attribute information. Finally, the node structure similarity and attribute similarity are fused and applied to learning the low-dimensional feature representation of nodes in the stacked denoising autoencoder. Comparison of experiments with representative algorithms in recent years on three social network datasets shows that the LFNE algorithm can achieve relatively significant results in node classification and link prediction with better network embedding performance. |
| first_indexed | 2024-04-13T09:21:17Z |
| format | Article |
| id | doaj.art-ad5e6a0570124a5aa7575f193c353cac |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-13T09:21:17Z |
| publishDate | 2022-11-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-ad5e6a0570124a5aa7575f193c353cac2022-12-22T02:52:35ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-11-0116112505251810.3778/j.issn.1673-9418.2104075Social Network Embedding Method Combining Node Attributes and Loop-Free PathWANG Benyu, GU Yijun, PENG Shufan0School of Information and Network Security, People’s Public Security University of China, Beijing 100032, ChinaNetwork embedding’s goal is to learn the low-dimensional node feature representation in the network. The learned features are used in various network analysis tasks, such as node classification, link prediction, community detection and recommendation, etc. The existing network embedding methods do not make full use of high-order structure information in social networks. Moreover, the correlation between structure information and node attribute information is not considered. The effect of these methods applied in the social network is not ideal. A social network embedding method combining loop-free path and attributes network embedding (LFNE) is proposed to solve these problems. The high-order structural similarity of nodes is calculated first based on the loop-free path between nodes to eliminate the influence of loop path and large-degree nodes on node structure similarity. This algorithm makes the network embedding method better integrate the high-order social network structure information. Then the node attributes similarity is calculated by combining the loop-free path similarity measurement index between nodes, and the correlation between social network structure information and attribute information is fully utilized to eliminate the noise in attribute information. Finally, the node structure similarity and attribute similarity are fused and applied to learning the low-dimensional feature representation of nodes in the stacked denoising autoencoder. Comparison of experiments with representative algorithms in recent years on three social network datasets shows that the LFNE algorithm can achieve relatively significant results in node classification and link prediction with better network embedding performance.http://fcst.ceaj.org/fileup/1673-9418/PDF/2104075.pdf|network embedding|social network|node attributes|loop-free path|stacked denoising autoencoder |
| spellingShingle | WANG Benyu, GU Yijun, PENG Shufan Social Network Embedding Method Combining Node Attributes and Loop-Free Path Jisuanji kexue yu tansuo |network embedding|social network|node attributes|loop-free path|stacked denoising autoencoder |
| title | Social Network Embedding Method Combining Node Attributes and Loop-Free Path |
| title_full | Social Network Embedding Method Combining Node Attributes and Loop-Free Path |
| title_fullStr | Social Network Embedding Method Combining Node Attributes and Loop-Free Path |
| title_full_unstemmed | Social Network Embedding Method Combining Node Attributes and Loop-Free Path |
| title_short | Social Network Embedding Method Combining Node Attributes and Loop-Free Path |
| title_sort | social network embedding method combining node attributes and loop free path |
| topic | |network embedding|social network|node attributes|loop-free path|stacked denoising autoencoder |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2104075.pdf |
| work_keys_str_mv | AT wangbenyuguyijunpengshufan socialnetworkembeddingmethodcombiningnodeattributesandloopfreepath |