{"type":"ScholarlyArticle","title":"Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity","authors":[{"name":"Yunsheng Bai","slug":"yunsheng-bai","given":"Yunsheng","family":"Bai","affiliation":"University of California , Los Angeles"},{"name":"Hao Ding","slug":"hao-ding","given":"Hao","family":"Ding","affiliation":"Purdue University"},{"name":"Yang Qiao","slug":"yang-qiao","given":"Yang","family":"Qiao","affiliation":"University of California , Los Angeles"},{"name":"Agustin Marinovic","slug":"agustin-marinovic","given":"Agustin","family":"Marinovic","affiliation":"University of California , Los Angeles"},{"name":"Ken Gu","slug":"ken-gu","given":"Ken","family":"Gu","affiliation":"University of California , Los Angeles"},{"name":"Ting Chen","slug":"ting-chen","given":"Ting","family":"Chen","affiliation":"University of California , Los Angeles"},{"name":"Yizhou Sun","slug":"yizhou-sun","given":"Yizhou","family":"Sun","affiliation":"University of California , Los Angeles"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"University of California , Los Angeles"}],"abstract":"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.","doi":"10.24963/ijcai.2019/275","url":"https://ijcai.org/proceedings/2019/275","pdf":"https://ijcai.org/proceedings/2019/0275.pdf","markdown":"https://ijcai.org/proceedings/2019/275.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/275","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":275,"pages":{"first":1988,"last":1994},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2019] Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang. 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