{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T19:02:49Z","timestamp":1749927769085},"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":[[2021,8]]},"abstract":"<jats:p>Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies. Moreover, the visualization of learned representation revealed that PHD strategy indeed empowers the model to learn graph-level knowledge like the molecular scaffold. These results have established PHD as a powerful and effective self-supervised learning strategy in graph-level representation learning.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/371","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2694-2700","source":"Crossref","is-referenced-by-count":8,"title":["Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Pengyong","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Tsinghua University, Beijing, China"},{"name":"Ping An Healthcare Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Li","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology, Beijing, China"},{"name":"Central University of Finance and Economics, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Qiao","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianggen","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guotong","family":"Xie","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology, Beijing, China"},{"name":"Ping An Health Cloud Company Limited, Shenzhen, China"},{"name":"Ping An International Smart City Technology Co., Ltd., Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:02:53Z","timestamp":1628679773000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/371"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/371","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}