{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:55:35Z","timestamp":1767182135629,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2017YFB1002502"],"award-info":[{"award-number":["2017YFB1002502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11063-022-11064-5","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T20:15:56Z","timestamp":1667506556000},"page":"4757-4776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Multi-layer Contrastive Graph Neural Networks"],"prefix":"10.1007","volume":"55","author":[{"given":"Shuhao","family":"Shi","sequence":"first","affiliation":[]},{"given":"Pengfei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-8230","authenticated-orcid":false,"given":"Bin","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"11064_CR1","unstructured":"Bachman P, Hjelm RD, Buchwalter W (2019) Learning representations by maximizing mutual information across views. Adv Neural Inf Process Syst 32"},{"key":"11064_CR2","unstructured":"Bojchevski A, G\u00fcnnemann S (2018) Deep gaussian embedding of graphs: unsupervised inductive learning via ranking. ICLR"},{"key":"11064_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3:1\u201327","journal-title":"Commun Stat Theory Methods"},{"key":"11064_CR4","unstructured":"Chen D, Lin Y, Li L, et\u00a0al (2020a) Distance-wise graph contrastive learning. arXiv preprint arXiv:2012.07437"},{"key":"11064_CR5","unstructured":"Chen T, Kornblith S, Norouzi M, et\u00a0al (2020b) A simple framework for contrastive learning of visual representations. arXiv:2002.05709"},{"key":"11064_CR6","unstructured":"Chen T, Kornblith S, Swersky K, et\u00a0al (2020c) Big self-supervised models are strong semi-supervised learners. Adv Neural Inf Process Syst 33:22,243\u201322,255"},{"key":"11064_CR7","unstructured":"Chen X, Fan H, Girshick RB, et\u00a0al (2020d) Improved baselines with momentum contrastive learning. arXiv:2003.04297"},{"key":"11064_CR8","unstructured":"Chen X, Zhang Y, Tsang I, et\u00a0al (2020e) Learning robust node representations on graphs. arXiv:2008.11416"},{"key":"11064_CR9","doi-asserted-by":"crossref","unstructured":"Davies DL, Bouldin D (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI-1:224\u2013227","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"11064_CR10","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428"},{"key":"11064_CR11","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, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"11064_CR12","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: NIPS"},{"key":"11064_CR13","doi-asserted-by":"crossref","unstructured":"Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C (Appl Stat) 28(1):100\u2013108","DOI":"10.2307\/2346830"},{"key":"11064_CR14","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, et\u00a0al (2020) Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9726\u20139735","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"11064_CR15","unstructured":"Hjelm RD, Fedorov A, Lavoie-Marchildon S, et\u00a0al (2019) Learning deep representations by mutual information estimation and maximization. ICLR arXiv:1808.06670"},{"key":"11064_CR16","unstructured":"Kipf T, Welling M (2016a) Variational graph auto-encoders. NeurIPS"},{"key":"11064_CR17","unstructured":"Kipf TN, Welling M (2016b) Semi-supervised classification with graph convolutional networks. ICLR arXiv:1609.02907"},{"key":"11064_CR18","doi-asserted-by":"crossref","unstructured":"Namata G, London B, Getoor L, et\u00a0al (2012) Query-driven active surveying for collective classification. In: 10th international workshop on mining and learning with graphs, p\u00a01","DOI":"10.1007\/978-1-4899-7502-7_44-1"},{"key":"11064_CR19","first-page":"259","volume":"2020","author":"Z Peng","year":"2020","unstructured":"Peng Z, Huang W, Luo M et al (2020) Graph representation learning via graphical mutual information maximization. Proc Web Conf 2020:259\u2013270","journal-title":"Proc Web Conf"},{"key":"11064_CR20","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, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"11064_CR21","doi-asserted-by":"crossref","unstructured":"Qiu J, Chen Q, Dong Y, et\u00a0al (2020) Gcc: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1150\u20131160","DOI":"10.1145\/3394486.3403168"},{"key":"11064_CR22","doi-asserted-by":"crossref","unstructured":"Ren Y, Bai J, Zhang J (2021) Label contrastive coding based graph neural network for graph classification. In: International conference on database systems for advanced applications. Springer, pp 123\u2013140","DOI":"10.1007\/978-3-030-73194-6_10"},{"key":"11064_CR23","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"P Rousseeuw","year":"1987","unstructured":"Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53\u201365","journal-title":"J Comput Appl Math"},{"key":"11064_CR24","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M et al (2008) Collective classification in network data. AI Mag 29:93\u2013106","journal-title":"AI Mag"},{"key":"11064_CR25","unstructured":"Shchur O, Mumme M, Bojchevski A, et\u00a0al (2018) Pitfalls of graph neural network evaluation. arXiv:1811.05868"},{"key":"11064_CR26","unstructured":"Sun FY, Hoffmann J, Tang J (2020) Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv:1908.01000"},{"key":"11064_CR27","unstructured":"Tschannen M, Djolonga J, Rubenstein PK, et\u00a0al (2020) On mutual information maximization for representation learning. ICLR arXiv:1907.13625"},{"key":"11064_CR28","unstructured":"van\u00a0den Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. CoRR arXiv:1807.03748"},{"key":"11064_CR29","unstructured":"Velickovic P, Cucurull G, Casanova A, et\u00a0al (2018) Graph attention networks. ICLR arXiv:1710.10903"},{"key":"11064_CR30","unstructured":"Velickovic P, Fedus W, Hamilton WL, et\u00a0al (2019) Deep graph infomax. ICLR"},{"key":"11064_CR31","doi-asserted-by":"crossref","unstructured":"Wan S, Pan S, Yang J, et\u00a0al (2021) Contrastive and generative graph convolutional networks for graph-based semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 10,049\u201310,057","DOI":"10.1609\/aaai.v35i11.17206"},{"key":"11064_CR32","unstructured":"Wu F, Souza A, Zhang T, et\u00a0al (2019) Simplifying graph convolutional networks. In: International conference on machine learning, PMLR, pp 6861\u20136871"},{"key":"11064_CR33","first-page":"1457","volume":"2020","author":"M Wu","year":"2020","unstructured":"Wu M, Pan S, Zhou C et al (2020) Unsupervised domain adaptive graph convolutional networks. Proc Web Conf 2020:1457\u20131467","journal-title":"Proc Web Conf"},{"key":"11064_CR34","doi-asserted-by":"crossref","unstructured":"Xu H, Zhang X, Li H, et\u00a0al (2022) Seed the views: Hierarchical semantic alignment for contrastive representation learning. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3176690"},{"key":"11064_CR35","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You Y, Chen T, Sui Y et al (2020) Graph contrastive learning with augmentations. Adv Neural Inf Process Syst 33:5812\u20135823","journal-title":"Adv Neural Inf Process Syst"},{"key":"11064_CR36","unstructured":"Zheng Y, Jin M, Liu Y, et\u00a0al (2022) From unsupervised to few-shot graph anomaly detection: a multi-scale contrastive learning approach. arXiv preprint arXiv:2202.05525"},{"key":"11064_CR37","unstructured":"Zhu Y, Xu Y, Yu F, et\u00a0al (2020) Deep graph contrastive representation learning. arXiv:2006.04131"},{"key":"11064_CR38","doi-asserted-by":"crossref","unstructured":"Zhu Y, Xu Y, Yu F, et\u00a0al (2021) Graph contrastive learning with adaptive augmentation. In: Proceedings of the web conference 2021","DOI":"10.1145\/3442381.3449802"},{"key":"11064_CR39","doi-asserted-by":"publisher","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","volume":"33","author":"M Zitnik","year":"2017","unstructured":"Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33:i190\u2013i198","journal-title":"Bioinformatics"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11064-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11064-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11064-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T16:53:46Z","timestamp":1690822426000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11064-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["11064"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11064-5","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,11,3]]},"assertion":[{"value":"16 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest between the authors regarding the manuscript preparation and submission.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors. Informal Consent Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"There is no consent to participate or any concerns in the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"There is no consent or any copyright needed to get concerns in the manuscript.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}