Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree
Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with heterophilic graphs. Although many novel methods h...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10579 |
| _version_ | 1797475463264731136 |
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| author | Rui Zhang Xin Li |
| author_facet | Rui Zhang Xin Li |
| author_sort | Rui Zhang |
| collection | DOAJ |
| description | Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with heterophilic graphs. Although many novel methods have recently been proposed to deal with heterophily, the effect of homophily and heterophily on classifying node pairs is not clearly separated in existing approaches and inevitably influences each other. To deal with various types of graphs more accurately, in this work we propose a new GCN-based model that leverages the explicitly estimated homophily and heterophily degree between node pairs and adaptively guides the propagation and aggregation of signed messages. We also design a pre-training process to learn homophily and heterophily degree from both original node attributes that are graph-agnostic and the localized graph structure information by using Deepwalk that reflects graph topology. Extensive experiments on eight real-world benchmarks demonstrate that the new approach achieves state-of-the-art results on three homophilic graph datasets and outperforms baselines on five heterophilic graph datasets. |
| first_indexed | 2024-03-09T20:45:27Z |
| format | Article |
| id | doaj.art-31d0879165614078be901dd8e2a92975 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:45:27Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-31d0879165614078be901dd8e2a929752023-11-23T22:46:59ZengMDPI AGApplied Sciences2076-34172022-10-0112201057910.3390/app122010579Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily DegreeRui Zhang0Xin Li1Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, ChinaShanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, ChinaGraph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with heterophilic graphs. Although many novel methods have recently been proposed to deal with heterophily, the effect of homophily and heterophily on classifying node pairs is not clearly separated in existing approaches and inevitably influences each other. To deal with various types of graphs more accurately, in this work we propose a new GCN-based model that leverages the explicitly estimated homophily and heterophily degree between node pairs and adaptively guides the propagation and aggregation of signed messages. We also design a pre-training process to learn homophily and heterophily degree from both original node attributes that are graph-agnostic and the localized graph structure information by using Deepwalk that reflects graph topology. Extensive experiments on eight real-world benchmarks demonstrate that the new approach achieves state-of-the-art results on three homophilic graph datasets and outperforms baselines on five heterophilic graph datasets.https://www.mdpi.com/2076-3417/12/20/10579graph convolutional networksheterophilic graphsnode classification |
| spellingShingle | Rui Zhang Xin Li Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree Applied Sciences graph convolutional networks heterophilic graphs node classification |
| title | Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree |
| title_full | Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree |
| title_fullStr | Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree |
| title_full_unstemmed | Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree |
| title_short | Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree |
| title_sort | graph convolutional networks guided by explicitly estimated homophily and heterophily degree |
| topic | graph convolutional networks heterophilic graphs node classification |
| url | https://www.mdpi.com/2076-3417/12/20/10579 |
| work_keys_str_mv | AT ruizhang graphconvolutionalnetworksguidedbyexplicitlyestimatedhomophilyandheterophilydegree AT xinli graphconvolutionalnetworksguidedbyexplicitlyestimatedhomophilyandheterophilydegree |