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Technol."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world applications. To this end, we present a novel unsupervised graph neural network model with Cluster-aware Self-training and Refining (\n            <jats:sans-serif>CLEAR<\/jats:sans-serif>\n            ). Specifically, in the proposed\n            <jats:sans-serif>CLEAR<\/jats:sans-serif>\n            model, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. To avoid degenerate solutions of clustering, we formulate the graph clustering problem as an optimal transport problem and leverage a balanced clustering strategy. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. Therefore, we propose to refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.\n          <\/jats:p>","DOI":"10.1145\/3608480","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T11:55:15Z","timestamp":1689076515000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2205-5304","authenticated-orcid":false,"given":"Yanqiao","family":"Zhu","sequence":"first","affiliation":[{"name":"University of California, Los Angeles, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2089-6767","authenticated-orcid":false,"given":"Yichen","family":"Xu","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7379-250X","authenticated-orcid":false,"given":"Feng","family":"Yu","sequence":"additional","affiliation":[{"name":"DP Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9233-3827","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-3577","authenticated-orcid":false,"given":"Shu","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1964","volume-title":"Advances in Neural Information Processing Systems 30","author":"Altschuler Jason","year":"2017","unstructured":"Jason Altschuler and Jonathan Weed. 2017. 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