{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T20:54:31Z","timestamp":1768251271915,"version":"3.49.0"},"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":[[2017,8]]},"abstract":"<jats:p>Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently. In this paper, we summarize most existing NRL methods into a unified two-step framework, including proximity matrix construction and dimension reduction. We focus on the analysis of proximity matrix construction step and conclude that an NRL method can be improved by exploring higher order proximities when building the proximity matrix. We propose Network Embedding Update (NEU) algorithm which implicitly approximates higher order proximities with theoretical approximation bound and can be applied on any NRL methods to enhance their performances. We conduct experiments on multi-label classification and link prediction tasks. Experimental results show that NEU can make a consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/544","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"3894-3900","source":"Crossref","is-referenced-by-count":99,"title":["Fast Network Embedding Enhancement via High Order Proximity Approximation"],"prefix":"10.24963","author":[{"given":"Cheng","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Maosong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"},{"name":"Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China"}]},{"given":"Zhiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"},{"name":"Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China"}]},{"given":"Cunchao","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"},{"name":"Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:54:27Z","timestamp":1501242867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/544"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/544","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}