{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:21Z","timestamp":1761176241549,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Graph Neural Networks (GNNs) have made significant progress in graph mining tasks, such as node classification. In particular, graph contrastive learning (GCL) methods based on data augmentation have been proven to effectively improve model performance. Existing GCL typically use two technologies: feature augmentation and structure augmentation. However, these technologies often cause excessive perturbation in the feature space when transforming node features. In addition, many structure augmentation techniques disrupt the graph topology, and many methods ignore the multi-hop neighbor information of nodes. To address these issues, we propose a new method called Spectral Feature Augmentation-based Multi-Hop Contrastive Learning (SMHCL), for node classification. First, we use spectral feature augmentation to implicitly inject noise into the singular values of node features, which avoids excessive perturbation in the feature space. Next, we generate multiple views by aggregating the multi-hop neighbor information of nodes. This approach effectively captures extensive neighbor information while preserving the graph\u2019s topology. Finally, we design a multi-hop contrastive loss function based on graph contrastive learning across multiple graphs. This enhances representation learning for the node classification task. Extensive experimental results show that SMHCL significantly outperforms existing GCL methods, both in label-rich and label-scarce scenarios.<\/jats:p>","DOI":"10.3233\/faia251207","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:39Z","timestamp":1761126879000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-Hop Contrastive Learning with Feature Augmentation for Node Classification"],"prefix":"10.3233","author":[{"given":"Xuxia","family":"Zeng","sequence":"first","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangquan","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"},{"name":"Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China"},{"name":"Guangxi Engineering Research Center of Educational Intelligent Technology, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuifang","family":"Zou","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilong","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longqing","family":"Du","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China"},{"name":"Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China"},{"name":"Guangxi Engineering Research Center of Educational Intelligent Technology, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:40Z","timestamp":1761126880000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251207","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}