{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:47:04Z","timestamp":1775144824174,"version":"3.50.1"},"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":[[2018,7]]},"abstract":"<jats:p>Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/428","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"3082-3088","source":"Crossref","is-referenced-by-count":132,"title":["Scalable Multiplex Network Embedding"],"prefix":"10.24963","author":[{"given":"Hongming","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of CSE, The Hong Kong University of Science and Technology"}]},{"given":"Liwei","family":"Qiu","sequence":"additional","affiliation":[{"name":"Tencent Technology (SZ) Co., Ltd., China."}]},{"given":"Lingling","family":"Yi","sequence":"additional","affiliation":[{"name":"Tencent Technology (SZ) Co., Ltd., China."}]},{"given":"Yangqiu","family":"Song","sequence":"additional","affiliation":[{"name":"Department of CSE, The Hong Kong University of Science and Technology"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:52:41Z","timestamp":1530769961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/428"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/428","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}