{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T10:27:42Z","timestamp":1756636062173},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"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":[[2024,1,12]]},"abstract":"<jats:p>Link prediction has important practical value in many fields, such as social networks, bioinformatics, citation networks, etc. However, existing link prediction methods mainly have two major problems: firstly, they neglect the attribute information of nodes or edges, which limits the accuracy and robustness of prediction; Secondly, when dealing with large-scale complex networks, especially those with rich attribute information, their performance still needs to be improved. To address these issues, the paper designs a citation network link prediction system based on Graph Attention Network (GAT). The system preprocesses the citation network data, constructs and trains a GAT model, and then uses heterogeneous graph convolution and temporal graph convolution to handle the heterogeneity and dynamism of the citation network. Then, a graph sampling strategy is used to handle large-scale citation networks, Finally, use the trained GAT model to predict possible links.<\/jats:p>","DOI":"10.3233\/faia231258","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:57:14Z","timestamp":1705064234000},"source":"Crossref","is-referenced-by-count":1,"title":["Design and Implementation on Citation Network Link Prediction System Based on GAT"],"prefix":"10.3233","author":[{"given":"Yiqin","family":"Bao","sequence":"first","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}]},{"given":"Wenbin","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu United Vocational and Technical College Suzhou Branch, China"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing Yilaichuang Electronic Technology Co., Ltd, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:57:16Z","timestamp":1705064236000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231258","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}