{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:33:15Z","timestamp":1723015995422},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Recent advances in the field of network embedding have shown that\nlow-dimensional network representation is playing\na critical role in network analysis. Most existing network embedding\nmethods encode the local proximity of a node,\nsuch as the first- and second-order proximities.\nWhile being efficient, these methods are short of leveraging the global structural information\nbetween nodes distant from each other.\nIn addition, most existing methods learn embeddings on one single fixed network,\nand thus cannot be generalized to unseen nodes or networks without retraining.\nIn this paper we present SPINE, a method that can jointly capture the local proximity and\nproximities at any distance, while being inductive to efficiently deal with unseen nodes or networks.\nExtensive experimental results on benchmark datasets\ndemonstrate the superiority of the proposed framework\nover the state of the art.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/333","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"2399-2405","source":"Crossref","is-referenced-by-count":10,"title":["SPINE: Structural Identity Preserved Inductive Network Embedding"],"prefix":"10.24963","author":[{"given":"Junliang","family":"Guo","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China"}]},{"given":"Linli","family":"Xu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China"}]},{"given":"Jingchang","family":"Liu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:48:37Z","timestamp":1564285717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/333"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/333","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}