{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:41:08Z","timestamp":1723016468978},"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":[[2022,7]]},"abstract":"<jats:p>A network can effectively depict close relationships among its nodes, with labels in a taxonomy describing the nodes' rich attributes. Network embedding aims at learning a representation vector for each node and label to preserve their proximity, while most existing methods suffer from serious underfitting when dealing with datasets with dense node-label links. For instance, a node could have dozens of labels describing its diverse properties, causing the single node vector overloaded and hard to fit all the labels. We propose HIerarchical Multi-vector Embedding (HIME), which solves the underfitting problem by adaptively learning multiple 'branch vectors' for each node to dynamically fit separate sets of labels in a hierarchy-aware embedding space. Moreover, a 'root vector' is learned for each node based on its branch vectors to better predict the sparse but valuable node-node links with the knowledge of its labels. Experiments reveal HIME\u2019s comprehensive advantages over existing methods on tasks such as proximity search, link prediction and hierarchical classification.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/408","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"2944-2950","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Vector Embedding on Networks with Taxonomies"],"prefix":"10.24963","author":[{"given":"Yue","family":"Fan","sequence":"first","affiliation":[{"name":"Peking University"}]},{"given":"Xiuli","family":"Ma","sequence":"additional","affiliation":[{"name":"Peking University"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:09:33Z","timestamp":1658142573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/408"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/408","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}