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Data"],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task (so-called pretext task). However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily. Motivated by this limitation, we propose a multi-view perspective and the usage of diverse pretext tasks to capture different signals in graphs into embeddings. A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode different types of signals, and a straightforward operation is proposed to maintain both the commodity and personalization in both the attribute and the structural levels. Extensive experiments on synthetic and real-world network datasets show that the node representations learned with MVGE achieve significant performance improvements in three different downstream tasks, especially on graphs with heterophily.<\/jats:p>","DOI":"10.1145\/3592858","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T12:18:50Z","timestamp":1681733930000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-view Graph Representation Learning Beyond Homophily"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7701-6908","authenticated-orcid":false,"given":"Bei","family":"Lin","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7686-7688","authenticated-orcid":false,"given":"You","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4983-5327","authenticated-orcid":false,"given":"Ning","family":"Gui","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1558-102X","authenticated-orcid":false,"given":"Zhuopeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5133-9691","authenticated-orcid":false,"given":"Zhiwu","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of High-speed Railway Construction Technology"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"21","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. 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