{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:11:17Z","timestamp":1769548277686,"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":[[2019,8]]},"abstract":"<jats:p>We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGraphEmb, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGraphEmb achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/275","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"1988-1994","source":"Crossref","is-referenced-by-count":45,"title":["Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity"],"prefix":"10.24963","author":[{"given":"Yunsheng","family":"Bai","sequence":"first","affiliation":[{"name":"University of California, Los Angeles"}]},{"given":"Hao","family":"Ding","sequence":"additional","affiliation":[{"name":"Purdue University"}]},{"given":"Yang","family":"Qiao","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"given":"Agustin","family":"Marinovic","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"given":"Ken","family":"Gu","sequence":"additional","affiliation":[{"name":"University of California Los Angeles"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"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":"2019-07-28T07:48:02Z","timestamp":1564300082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/275"}},"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\/275","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}