{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T16:44:58Z","timestamp":1696092298612},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>Graph classification is a classic problem with practical applications in many real-life scenes. Existing graph neural networks, including GCN, GAT, and GIN, are proposed to extract useful features from complex graph structures. However, most existing methods\u2019 feature extraction and aggregation inevitably mix the useful and redundant features, which will disturb the final classification performance. In this paper, to handle the above drawback, we put forward the Local Structural Separation Hypergraph Convolutional Neural Network (LoSS) based on two discoveries: most graph classification tasks only focus on a few groups of adjacent nodes, and different categories have their specific high response bits in graph embeddings. In LoSS, we first decouple the original graph into different hypergraphs and aggregate the features in each substructure, which aims to find useful features for the final classification. Next, the low-correlation feature suppression strategy is devised to suppress the irrelevant node-level and bit-level features in the forward inference process, effectively reducing the disturbance of redundant features. Experiments on five datasets show that the proposed LoSS can effectively locate and aggregate useful hypergraph features and achieve SOTA performance compared with existing methods.<\/jats:p>","DOI":"10.3233\/faia230379","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:10:42Z","timestamp":1695978642000},"source":"Crossref","is-referenced-by-count":0,"title":["LoSS: Local Structural Separation Hypergraph Convolutional Neural Network"],"prefix":"10.3233","author":[{"given":"Bingde","family":"Hu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"},{"name":"ZJU-Bangsun Joint Research Center"},{"name":"Shanghai Institute for Advanced Study of Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zunlei","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Software Technology, Zhejiang University"},{"name":"ZJU-Bangsun Joint Research Center"},{"name":"Shanghai Institute for Advanced Study of Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"},{"name":"ZJU-Bangsun Joint Research Center"},{"name":"Shanghai Institute for Advanced Study of Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Bangsun Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230379","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:10:42Z","timestamp":1695978642000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230379","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}