Ke-Jia Zhang;Xiao Ding;Bing-Bing Xiang;Hai-Feng Zhang;Zhong-Kui Bao
{"title":"Extracting Higher Order Topological Semantic via Motif-Based Deep Graph Neural Networks","authors":"Ke-Jia Zhang;Xiao Ding;Bing-Bing Xiang;Hai-Feng Zhang;Zhong-Kui Bao","doi":"10.1109/TCSS.2024.3372775","DOIUrl":null,"url":null,"abstract":"Graph neural networks (GNNs) are efficient techniques for learning graph representations and have shown remarkable success in tackling diverse graph-related tasks. However, in the context of the neighborhood aggregation paradigm, conventional GNNs have limited capabilities in capturing the higher order structures and topological semantics of graphs. Researchers have attempted to overcome this limitation by designing new GNNs that explore the impacts of motifs to capture potentially higher order graph information. However, existing motif-based GNNs often ignore lower order connectivity patterns such as nodes and edges, which leads to poor representation of sparse networks. To address these limitations, we propose an innovative approach. First, we design convolution kernels on both motif-based and simple graphs. Second, we introduce a multilevel graph convolution framework for extracting higher order topological semantics of graphs. Our approach overcomes the limitations of prior methods, demonstrating state-of-the-art performance in downstream tasks with excellent scalability. Extensive experiments on real-world datasets validate the effectiveness of our proposed method.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10477438/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Graph neural networks (GNNs) are efficient techniques for learning graph representations and have shown remarkable success in tackling diverse graph-related tasks. However, in the context of the neighborhood aggregation paradigm, conventional GNNs have limited capabilities in capturing the higher order structures and topological semantics of graphs. Researchers have attempted to overcome this limitation by designing new GNNs that explore the impacts of motifs to capture potentially higher order graph information. However, existing motif-based GNNs often ignore lower order connectivity patterns such as nodes and edges, which leads to poor representation of sparse networks. To address these limitations, we propose an innovative approach. First, we design convolution kernels on both motif-based and simple graphs. Second, we introduce a multilevel graph convolution framework for extracting higher order topological semantics of graphs. Our approach overcomes the limitations of prior methods, demonstrating state-of-the-art performance in downstream tasks with excellent scalability. Extensive experiments on real-world datasets validate the effectiveness of our proposed method.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.