{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:01:13Z","timestamp":1771102873007,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U1636207"],"award-info":[{"award-number":["No. U1636207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U1936213"],"award-info":[{"award-number":["No. U1936213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation","award":["IIS-1763365"],"award-info":[{"award-number":["IIS-1763365"]}]},{"name":"National Science Foundation","award":["IIS-2106972"],"award-info":[{"award-number":["IIS-2106972"]}]},{"DOI":"10.13039\/100007707","name":"University of California Davis","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007707","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10115-021-01635-8","type":"journal-article","created":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T14:04:51Z","timestamp":1642601091000},"page":"235-260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Scalable self-supervised graph representation learning via enhancing and contrasting subgraphs"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-8652","authenticated-orcid":false,"given":"Yizhu","family":"Jiao","sequence":"first","affiliation":[]},{"given":"Yun","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tianqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yangyong","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"key":"1635_CR1","unstructured":"Abu-El-Haija S, Perozzi B, Kapoor A, Alipourfard N, Lerman K, Harutyunyan H, Steeg GV, Galstyan A (2019) Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing. arXiv preprint arXiv:1905.00067"},{"key":"1635_CR2","unstructured":"Asano YM, Rupprecht C, Vedaldi A (2019) A critical analysis of self-supervision, or what we can learn from a single image. arXiv preprint arXiv:1904.13132"},{"key":"1635_CR3","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247"},{"key":"1635_CR4","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709"},{"key":"1635_CR5","unstructured":"Dupont P, Callut J, Dooms G, Monette JN, Deville Y, Sainte B (2006) Relevant subgraph extraction from random walks in a graph. Universite Catholique de Louvain, UCL\/INGI, Number RR, p 7"},{"key":"1635_CR6","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch geometric. In: ICLR workshop on representation learning on graphs and manifolds"},{"key":"1635_CR7","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728"},{"key":"1635_CR8","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1635_CR9","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024\u20131034"},{"key":"1635_CR10","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584"},{"key":"1635_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"1635_CR12","doi-asserted-by":"crossref","unstructured":"Jeh G, Widom J (2003) Scaling personalized web search. In: Proceedings of the 12th international conference on World Wide Web, pp 271\u2013279","DOI":"10.1145\/775152.775191"},{"key":"1635_CR13","doi-asserted-by":"crossref","unstructured":"Jiao Y, Xiong Y, Zhang J, Zhu Y (2019) Collective link prediction oriented network embedding with hierarchical graph attention. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 419\u2013428","DOI":"10.1145\/3357384.3357990"},{"key":"1635_CR14","doi-asserted-by":"crossref","unstructured":"Jing L, Tian Y (2020) Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"1635_CR15","doi-asserted-by":"crossref","unstructured":"Ketkar N (2017) Introduction to pytorch. In: Deep learning with python. Springer, pp 195\u2013208","DOI":"10.1007\/978-1-4842-2766-4_12"},{"key":"1635_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1635_CR17","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"1635_CR18","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"1635_CR19","unstructured":"Lee J, Lee I, Kang J (2019) Self-attention graph pooling. arXiv preprint arXiv:1904.08082"},{"key":"1635_CR20","unstructured":"Liao R, Zhao Z, Urtasun R, Zemel RS (2019) Lanczosnet: multi-scale deep graph convolutional networks. arXiv preprint arXiv:1901.01484"},{"key":"1635_CR21","doi-asserted-by":"crossref","unstructured":"Meng L, Yang Bai J, Zhang J (2019) Latte: application oriented social network embedding. In: 2019 IEEE international conference on big data (Big Data), pp 1169\u20131174","DOI":"10.1109\/BigData47090.2019.9006488"},{"key":"1635_CR22","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"1635_CR23","doi-asserted-by":"crossref","unstructured":"Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, Huang J (2020) Graph representation learning via graphical mutual information maximization. In: Proceedings of the web conference, pp 259\u2013270","DOI":"10.1145\/3366423.3380112"},{"key":"1635_CR24","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1635_CR25","unstructured":"Qu M, Bengio Y, Tang J (2019) Gmnn: graph markov neural networks. In: International conference on machine learning, pp 5241\u20135250"},{"key":"1635_CR26","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) Bpr: bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618"},{"key":"1635_CR27","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"1","key":"1635_CR28","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60","journal-title":"J Big Data"},{"key":"1635_CR29","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"1635_CR30","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks. In: CoRR. arXiv:1710.10903"},{"key":"1635_CR31","unstructured":"Veli\u010dkovi\u0107 P, Fedus W, Hamilton WL, Li\u00f2 P, Bengio Y, Hjelm RD (2018) Deep graph infomax. arXiv preprint arXiv:1809.10341"},{"key":"1635_CR32","doi-asserted-by":"crossref","unstructured":"Wang S, He L, Cao B, Lu CT, Yu PS, Ragin AB (2017) Structural deep brain network mining. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 475\u2013484","DOI":"10.1145\/3097983.3097988"},{"key":"1635_CR33","unstructured":"Wu F, Souza Jr AH, Zhang T, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: ICML"},{"key":"1635_CR34","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826"},{"key":"1635_CR35","unstructured":"Yizhu J, Yun X, Jiawei Z, Yao Z, Tianqi Z, Yangyong Z (2020) Sub-graph contrast for scalable self-supervised graph representation learning. In: 2020 IEEE international conference on data mining (ICDM). IEEE"},{"key":"1635_CR36","unstructured":"Zeng H, Zhou H, Srivastava A, Kannan R, Prasanna V (2019) Graphsaint: graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931"},{"key":"1635_CR37","unstructured":"Zhang J, Meng L (2019) Gresnet: graph residual network for reviving deep gnns from suspended animation. arXiv abs\/1909.05729"},{"key":"1635_CR38","unstructured":"Zhang J, Zhang H, Sun L, Xia C (2020) Graph-bert: only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01635-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-021-01635-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01635-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T16:35:47Z","timestamp":1643301347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-021-01635-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1635"],"URL":"https:\/\/doi.org\/10.1007\/s10115-021-01635-8","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"16 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}