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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable effectiveness in various prediction tasks. However, most network representation learning algorithms are based on homogeneous networks and static networks for link prediction that do not consider rich semantic and dynamic information. Additionally, existing dynamic network representation learning methods neglect the neighborhood interaction structure of the node. In this work, we design a neighbor-enhanced dynamic heterogeneous attributed network embedding method (NeiDyHNE) for link prediction. In light of the impressive achievements of the heuristic methods, we learn the information of common neighbors and neighbors\u2019 interaction in heterogeneous networks to preserve the neighbors proximity and common neighbors proximity. NeiDyHNE encodes the attributes and neighborhood structure of nodes as well as the evolutionary features of the dynamic network. More specifically, NeiDyHNE consists of the hierarchical structure attention module and the convolutional temporal attention module. The hierarchical structure attention module captures the rich features and semantic structure of nodes. The convolutional temporal attention module captures the evolutionary features of the network over time in dynamic heterogeneous networks. We evaluate our method and various baseline methods on the dynamic link prediction task. Experimental results demonstrate that our method is superior to baseline methods in terms of accuracy.<\/jats:p>","DOI":"10.1145\/3676559","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T15:09:49Z","timestamp":1720105789000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Neighbor-Enhanced Representation Learning for Link Prediction in Dynamic Heterogeneous Attributed Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9543-7026","authenticated-orcid":false,"given":"Xiangyu","family":"Wei","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5974-1589","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China and Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1632-7238","authenticated-orcid":false,"given":"Chongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Henan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3180-7347","authenticated-orcid":false,"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Jiangsu, Chin"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3790-2708","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Multi-Dimensional Perception Technology, Application and Cybersecurity, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4056-9755","authenticated-orcid":false,"given":"Yaguan","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Science, Zhejiang University of Science and Technology, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3688-873X","authenticated-orcid":false,"given":"Zhen","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6461-9684","authenticated-orcid":false,"given":"Chunhua","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Division of Computer Science, University of Aizu, Aizuwakamatsu, Japan"}]}],"member":"320","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1145\/1935826.1935914","volume-title":"Proceedings of the 4th ACM International Conference on Web Search and Data Mining","author":"Backstrom Lars","year":"2011","unstructured":"Lars Backstrom and Jure Leskovec. 2011. 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