Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification Yan Pang, Yan Pang orcid.org/0000-0002-6483-8326 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this authorTeng Huang, Teng Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this authorZhen Wang, Zhen Wang orcid.org/0000-0002-8637-8375 Research Center for Big Data Intelligence, Zhejiang Lab, Hangzhou, Zhejiang, ChinaSearch for more papers by this authorJianwei Li, Jianwei Li orcid.org/0000-0002-5372-223X Department of Computer Science, San Jose State University, San Jose, California, USASearch for more papers by this authorPoorya Hosseini, Poorya Hosseini orcid.org/0000-0001-6929-8106 Applied Physics Laboratory, Johns Hopkins University, El Segundo, California, USASearch for more papers by this authorJi Zhang, Ji Zhang orcid.org/0000-0001-7167-6970 School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, AustraliaSearch for more papers by this authorChao Liu, Corresponding Author Chao Liu chao.liu@ucdenver.edu orcid.org/0000-0002-8703-4232 Department of Electrical Engineering, University of Colorado Denver, Denver, Colorado, USA Correspondence Chao Liu, Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA. Email: chao.liu@ucdenver.eduSearch for more papers by this authorShan Ai, Shan Ai Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this author Yan Pang, Yan Pang orcid.org/0000-0002-6483-8326 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this authorTeng Huang, Teng Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this authorZhen Wang, Zhen Wang orcid.org/0000-0002-8637-8375 Research Center for Big Data Intelligence, Zhejiang Lab, Hangzhou, Zhejiang, ChinaSearch for more papers by this authorJianwei Li, Jianwei Li orcid.org/0000-0002-5372-223X Department of Computer Science, San Jose State University, San Jose, California, USASearch for more papers by this authorPoorya Hosseini, Poorya Hosseini orcid.org/0000-0001-6929-8106 Applied Physics Laboratory, Johns Hopkins University, El Segundo, California, USASearch for more papers by this authorJi Zhang, Ji Zhang orcid.org/0000-0001-7167-6970 School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, AustraliaSearch for more papers by this authorChao Liu, Corresponding Author Chao Liu chao.liu@ucdenver.edu orcid.org/0000-0002-8703-4232 Department of Electrical Engineering, University of Colorado Denver, Denver, Colorado, USA Correspondence Chao Liu, Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA. Email: chao.liu@ucdenver.eduSearch for more papers by this authorShan Ai, Shan Ai Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch for more papers by this author First published: 25 July 2022 https://doi.org/10.1002/int.22966 Chao Liu and Shan Ali are co-senior authors who made equal contributions to manuscript. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Graph neural networks (GNNs) can be effectively applied to solve many real-world problems across widely diverse fields. Their success is inseparable from the message-passing mechanisms evolving over the years. However, current mechanisms treat all node features equally at the macro-level (node-level), and the optimal aggregation method has not yet been explored. In this paper, we propose a new GNN called Graph Decipher (GD), which transparentizes the message flows of node features from micro-level (feature-level) to global-level and boosts the performance on node classification tasks. Besides, to reduce the computational burden caused by investigating message-passing, only the relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category-oriented manner. Experiments on 10 node classification data sets show that GD achieves state-of-the-art performance while imposing a substantially lower computational cost. Additionally, since GD has the ability to explore the representative node attributes by category, it can also be applied to imbalanced node classification on multiclass graph data sets. Open Research DATA AVAILABILITY STATEMENT No. Research data are not shared. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation