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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the clustering network under the parametric condition\u2014predefining the true number of clusters (\n            <jats:italic>\n              K\n              <jats:sub>tr<\/jats:sub>\n            <\/jats:italic>\n            ). However,\n            <jats:italic>\n              K\n              <jats:sub>tr<\/jats:sub>\n            <\/jats:italic>\n            is inaccessible in pure unsupervised scenarios, in which existing methods are incapable of inferring the number of clusters (\n            <jats:italic>K<\/jats:italic>\n            ), causing limited feasibility. This article proposes the first Parameter-Agnostic Deep Graph Clustering method (PADGC), which consists of two core modules:\n            <jats:italic>K<\/jats:italic>\n            -guidence clustering and topological-hierarchical inference, to infer\n            <jats:italic>K<\/jats:italic>\n            efficiently and gain impressive clustering predictions. Specifically,\n            <jats:italic>K<\/jats:italic>\n            -guidence clustering is employed to optimize the cluster assignments and discriminative embeddings in a mutual promotion manner under the latest updated\n            <jats:italic>K<\/jats:italic>\n            , even though\n            <jats:italic>K<\/jats:italic>\n            may deviate from\n            <jats:italic>\n              K\n              <jats:sub>tr<\/jats:sub>\n            <\/jats:italic>\n            . In turn, such optimized cluster assignments are utilized to explore more accurate\n            <jats:italic>K<\/jats:italic>\n            in the topological-hierarchical inference, which can split the dispersive clusters and merge the coupled ones. In this way, these two modules are complementarily optimized until generating the final convergent\n            <jats:italic>K<\/jats:italic>\n            and discriminative cluster assignments. Extensive experiments on several benchmarks, including graphs and images, can demonstrate the superiority of our method. The mean values of our inferred\n            <jats:italic>K<\/jats:italic>\n            , in 11 out of 12 datasets, deviates from\n            <jats:italic>\n              K\n              <jats:sub>tr<\/jats:sub>\n            <\/jats:italic>\n            by less than 1. Our method can also achieve competitive clustering effects with existing parametric deep graph clustering.\n          <\/jats:p>","DOI":"10.1145\/3633783","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T15:50:43Z","timestamp":1701100243000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Parameter-Agnostic Deep Graph Clustering"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0365-7714","authenticated-orcid":false,"given":"Han","family":"Zhao","sequence":"first","affiliation":[{"name":"School of electronic Engineering, Xidian University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0405-6816","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of electronic Engineering, Xidian University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2620-3247","authenticated-orcid":false,"given":"Cheng","family":"Deng","sequence":"additional","affiliation":[{"name":"School of electronic Engineering, Xidian University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107589"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342871"},{"key":"e_1_3_1_4_2","first-page":"874","volume-title":"Proc. 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