Deep graph representation learning: methods, applications, and challenges
Graph representation learning has emerged as a crucial research area in recent years, aiming to generate vector representations that accurately capture the structure and features of graphs. These vectors play a vital role in downstream tasks such as node classification, link prediction, and anomaly...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257984&Fpath=home&index=0 |
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| _version_ | 1850136777252143104 |
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| author | ZHANG Xulong QU Xiaoyang XIAO Chunguang WANG Jianzong |
| author_facet | ZHANG Xulong QU Xiaoyang XIAO Chunguang WANG Jianzong |
| author_sort | ZHANG Xulong |
| collection | DOAJ |
| description | Graph representation learning has emerged as a crucial research area in recent years, aiming to generate vector representations that accurately capture the structure and features of graphs. These vectors play a vital role in downstream tasks such as node classification, link prediction, and anomaly detection. This paper presents a comprehensive survey of graph representation learning methods, categorizing them into traditional graph embedding methods and Graph Neural Network (GNN) based approaches. We discuss various techniques within these categories, including matrix factorization, random walks, graph convolutional networks, and graph Transformers. Furthermore, we delve into the specific applications of GNN in heterogeneous graph embedding, encompassing both static and dynamic aspects. This paper also explores the challenges and future directions of graph representation learning, including scalability and dynamics. |
| format | Article |
| id | doaj-art-fe99ae0f84f14a4c8bff354524474c3e |
| institution | OA Journals |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-fe99ae0f84f14a4c8bff354524474c3e2025-08-20T02:31:02ZzhoChina InfoCom Media Group大数据2096-02712025-01-01123109257984Deep graph representation learning: methods, applications, and challengesZHANG XulongQU XiaoyangXIAO ChunguangWANG JianzongGraph representation learning has emerged as a crucial research area in recent years, aiming to generate vector representations that accurately capture the structure and features of graphs. These vectors play a vital role in downstream tasks such as node classification, link prediction, and anomaly detection. This paper presents a comprehensive survey of graph representation learning methods, categorizing them into traditional graph embedding methods and Graph Neural Network (GNN) based approaches. We discuss various techniques within these categories, including matrix factorization, random walks, graph convolutional networks, and graph Transformers. Furthermore, we delve into the specific applications of GNN in heterogeneous graph embedding, encompassing both static and dynamic aspects. This paper also explores the challenges and future directions of graph representation learning, including scalability and dynamics.http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257984&Fpath=home&index=0graph neural networksgraph embeddingheterogeneous graphdeep learning |
| spellingShingle | ZHANG Xulong QU Xiaoyang XIAO Chunguang WANG Jianzong Deep graph representation learning: methods, applications, and challenges 大数据 graph neural networks graph embedding heterogeneous graph deep learning |
| title | Deep graph representation learning: methods, applications, and challenges |
| title_full | Deep graph representation learning: methods, applications, and challenges |
| title_fullStr | Deep graph representation learning: methods, applications, and challenges |
| title_full_unstemmed | Deep graph representation learning: methods, applications, and challenges |
| title_short | Deep graph representation learning: methods, applications, and challenges |
| title_sort | deep graph representation learning methods applications and challenges |
| topic | graph neural networks graph embedding heterogeneous graph deep learning |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257984&Fpath=home&index=0 |
| work_keys_str_mv | AT zhangxulong deepgraphrepresentationlearningmethodsapplicationsandchallenges AT quxiaoyang deepgraphrepresentationlearningmethodsapplicationsandchallenges AT xiaochunguang deepgraphrepresentationlearningmethodsapplicationsandchallenges AT wangjianzong deepgraphrepresentationlearningmethodsapplicationsandchallenges |