{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:29:35Z","timestamp":1763810975624,"version":"3.41.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T00:00:00Z","timestamp":1680825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["42050105, 62020106005, 62061146002, and 61960206002"],"award-info":[{"award-number":["42050105, 62020106005, 62061146002, and 61960206002"]}]},{"name":"100-Talents Program of Xinhua News Agency, and the Program of Shanghai Academic\/Technology Research Leader","award":["18XD1401800"],"award-info":[{"award-number":["18XD1401800"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>\n            Self-supervised graph-level representation learning has recently received considerable attention. Given varied input distributions, jointly learning graphs\u2019 unique and common features is vital to downstream tasks. Inspired by graph contrastive learning (GCL), which targets maximizing the agreement between graph representations from different views, we propose an\n            <jats:underline>Ada<\/jats:underline>\n            ptive self-supervised framework, Ada-MIP, considering both\n            <jats:underline>M<\/jats:underline>\n            utual\n            <jats:underline>I<\/jats:underline>\n            nformation between views (unique features) and inter-graph\n            <jats:underline>P<\/jats:underline>\n            roximity (common features). Specifically, Ada-MIP learns graphs\u2019 unique information through a learnable and probably injective augmenter, which can acquire more adaptive views compared to the augmentation strategies applied by existing GCL methods; to learn graphs\u2019 common information, we employ graph kernels to calculate graphs\u2019 proximity and learn graph representations among which the precomputed proximity is preserved. By sharing a global encoder, graphs\u2019 unique and common information can be well integrated into the graph representations learned by Ada-MIP. Ada-MIP is also extendable to semi-supervised scenarios, with our experiments confirming its superior performance in both unsupervised and semi-supervised tasks.\n          <\/jats:p>","DOI":"10.1145\/3568165","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T11:51:19Z","timestamp":1667994679000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Ada-MIP: Adaptive Self-supervised Graph Representation Learning via Mutual Information and Proximity Optimization"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5750-8401","authenticated-orcid":false,"given":"Yuyang","family":"Ren","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9671-2058","authenticated-orcid":false,"given":"Haonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4446-4427","authenticated-orcid":false,"given":"Peng","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7796-9168","authenticated-orcid":false,"given":"Luoyi","family":"Fu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2315-4219","authenticated-orcid":false,"given":"Xinde","family":"Cao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0357-8356","authenticated-orcid":false,"given":"Xinbing","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6934-1685","authenticated-orcid":false,"given":"Guihai","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-4163","authenticated-orcid":false,"given":"Fei","family":"Long","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence Production Technology and Systems, Xinhua News Agency, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3331-2302","authenticated-orcid":false,"given":"Chenghu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing Shi, China"}]}],"member":"320","published-online":{"date-parts":[[2023,4,7]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93037-4_14"},{"key":"e_1_3_2_3_1","first-page":"684","volume-title":"Proceedings of the 48th Annual ACM Symposium on Theory of Computing","author":"Babai L\u00e1szl\u00f3","year":"2016","unstructured":"L\u00e1szl\u00f3 Babai. 2016. 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