{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:34Z","timestamp":1760245054157},"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>We propose weighted inner product similarity (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive definite, conditionally positive definite, and indefinite kernels. WIPS is free from similarity model selection, since it can learn any similarity models such as cosine similarity, negative Poincar\u00e9 distance and negative Wasserstein distance. Our experiments show that the proposed method can learn high-quality distributed representations of nodes from real datasets, leading to an accurate approximation of similarities as well as high performance in inductive tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/699","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5031-5038","source":"Crossref","is-referenced-by-count":3,"title":["Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities"],"prefix":"10.24963","author":[{"given":"Geewook","family":"Kim","sequence":"first","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"},{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akifumi","family":"Okuno","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"},{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuki","family":"Fukui","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"},{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hidetoshi","family":"Shimodaira","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"},{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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":"2019-07-28T07:51:09Z","timestamp":1564300269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/699"}},"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\/699","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}