Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Sparse-Dyn: Sparse dynamic graph multirepresentation learning via event-based sparse temporal attention network 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 authorAi Shan, Ai Shan 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 authorMengyu Wang, Mengyu Wang orcid.org/0000-0003-0707-0071 Department of Information Science and Technology, Osaka University, Osaka, JapanSearch for more papers by this authorJianwei Li, Jianwei Li Department of Computer Science, San Jose State University, San Jose, 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 authorTeng Huang, Teng Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch 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 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 authorAi Shan, Ai Shan 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 authorMengyu Wang, Mengyu Wang orcid.org/0000-0003-0707-0071 Department of Information Science and Technology, Osaka University, Osaka, JapanSearch for more papers by this authorJianwei Li, Jianwei Li Department of Computer Science, San Jose State University, San Jose, 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 authorTeng Huang, Teng Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, ChinaSearch 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 author First published: 25 July 2022 https://doi.org/10.1002/int.22967 Teng Huang is 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 Dynamic graph neural networks (DGNNs) have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss, or continuous learning which involves heavy computation. In this study, we proposed a novel DGNN, sparse dynamic (Sparse-Dyn). It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding using snapshots which cause information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through structural neighborhoods and temporal dynamics. Since the fully connected attention conjunction is simplified, the computation cost is far lower than the current state-of-the-art. Link prediction experiments are conducted on both continuous and discrete graph data sets. By comparing several state-of-the-art graph embedding baselines, the experimental results demonstrate that Sparse-Dyn has a faster inference speed while having competitive performance. Open Research DATA AVAILABILITY STATEMENT No. Research data are not shared. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation