Measuring and sampling: A metric‐guided subgraph learning framework for graph neural network

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Measuring and sampling: A metric-guided subgraph learning framework for graph neural network Jiyang Bai, Jiyang Bai orcid.org/0000-0002-0621-8815 Department of Computer Science, Florida State University, Tallahassee, Florida, USASearch for more papers by this authorYuxiang Ren, Corresponding Author Yuxiang Ren yuxiang@ifmlab.org IFM Lab, Department of Computer Science, Florida State University, Tallahassee, Florida, USA Correspondence Yuxiang Ren, Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA. Email: yuxiang@ifmlab.orgSearch for more papers by this authorJiawei Zhang, Jiawei Zhang IFM Lab, Department of Computer Science, University of California, Davis, Davis, California, USASearch for more papers by this author Jiyang Bai, Jiyang Bai orcid.org/0000-0002-0621-8815 Department of Computer Science, Florida State University, Tallahassee, Florida, USASearch for more papers by this authorYuxiang Ren, Corresponding Author Yuxiang Ren yuxiang@ifmlab.org IFM Lab, Department of Computer Science, Florida State University, Tallahassee, Florida, USA Correspondence Yuxiang Ren, Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA. Email: yuxiang@ifmlab.orgSearch for more papers by this authorJiawei Zhang, Jiawei Zhang IFM Lab, Department of Computer Science, University of California, Davis, Davis, California, USASearch for more papers by this author First published: 28 April 2022 https://doi.org/10.1002/int.22891 Jiyang Bai and Yuxiang Ren should be considered joint first author. 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) have shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However, many GNNs encounter problems in effectiveness and efficiency when they are designed with a deeper network structure or handle large-sized graphs. Several sampling algorithms have been proposed for improving and accelerating the training of GNNs, yet they ignore understanding the source of GNNs performance gain. The measurement of information within graph data can help the sampling algorithms to keep high-value information while removing redundant information and even noise. In this paper, we propose a Metric-Guided (MeGuide) subgraph learning framework for GNNs. MeGuide employs two novel metrics: Feature Smoothness and Connection Failure Distance to guide the subgraph sampling and mini-batch based training. Feature Smoothness is designed for analyzing the feature of nodes to retain the most valuable information, while Connection Failure Distance can measure the structural information to control the size of subgraphs. We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple data sets. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are openly available at https://github.com/tkipf/gcn/tree/master/gcn/data, and https://github.com/GraphSAINT/GraphSAINT, reference number.14, 19, 43 Early ViewOnline Version of Record before inclusion in an issue RelatedInformation