A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916657404837888 |
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| author | Gong, Chenghua Cheng, Yao Yu, Jianxiang Xu, Can Shan, Caihua Luo, Siqiang Li, Xiang |
| author_facet | Gong, Chenghua Cheng, Yao Yu, Jianxiang Xu, Can Shan, Caihua Luo, Siqiang Li, Xiang |
| contents | Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar features, have recently attracted significant attention and found many real-world applications. Meanwhile, increasing efforts have been made to advance learning from graphs with heterophily. Various graph heterophily measures, benchmark datasets, and learning paradigms are emerging rapidly. In this survey, we comprehensively review existing works on learning from graphs with heterophily. First, we overview over 500 publications, of which more than 340 are directly related to heterophilic graphs. After that, we survey existing metrics of graph heterophily and list recent benchmark datasets. Further, we systematically categorize existing methods based on a hierarchical taxonomy including GNN models, learning paradigms and practical applications. In addition, broader topics related to graph heterophily are also included. Finally, we discuss the primary challenges of existing studies and highlight promising avenues for future research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_09769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions Gong, Chenghua Cheng, Yao Yu, Jianxiang Xu, Can Shan, Caihua Luo, Siqiang Li, Xiang Social and Information Networks Artificial Intelligence Machine Learning Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar features, have recently attracted significant attention and found many real-world applications. Meanwhile, increasing efforts have been made to advance learning from graphs with heterophily. Various graph heterophily measures, benchmark datasets, and learning paradigms are emerging rapidly. In this survey, we comprehensively review existing works on learning from graphs with heterophily. First, we overview over 500 publications, of which more than 340 are directly related to heterophilic graphs. After that, we survey existing metrics of graph heterophily and list recent benchmark datasets. Further, we systematically categorize existing methods based on a hierarchical taxonomy including GNN models, learning paradigms and practical applications. In addition, broader topics related to graph heterophily are also included. Finally, we discuss the primary challenges of existing studies and highlight promising avenues for future research. |
| title | A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions |
| topic | Social and Information Networks Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.09769 |