{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:33:50Z","timestamp":1758303230664,"version":"3.44.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72374210"],"award-info":[{"award-number":["72374210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10489-025-06660-6","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:30:15Z","timestamp":1750224615000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GraphCKSA: Innovative dual-strategy GNN for imbalanced node classification with CENN-KCQ resampling and dual-view edge optimization"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1212-4855","authenticated-orcid":false,"given":"Liying","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7765-5256","authenticated-orcid":false,"given":"Lumeng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0246-0831","authenticated-orcid":false,"given":"Tianbo","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Zhiguang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xinzhu","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"6660_CR1","unstructured":"(2024) Pubmed. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/. Accessed: 2024-05-12"},{"key":"6660_CR2","doi-asserted-by":"crossref","unstructured":"Ando S, Huang CY (2017) Deep over-sampling framework for classifying imbalanced data. arXiv:1704.07515","DOI":"10.1007\/978-3-319-71249-9_46"},{"issue":"6","key":"6660_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran K, Deisenroth MP, Brundage M et al (2017) Deep reinforcement learning: A brief survey. IEEE Signal Process Mag 34(6):26\u201338","journal-title":"IEEE Signal Process Mag"},{"issue":"1","key":"6660_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter 6(1):20\u201329","journal-title":"ACM SIGKDD explorations newsletter"},{"key":"6660_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO et al (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"6660_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and smote. Inf Sci 465:1\u201320","journal-title":"Inf Sci"},{"key":"6660_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and smote. Information sciences 465:1\u201320","journal-title":"Information sciences"},{"key":"6660_CR8","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, NIPS\u201917, pp 1025\u20131035"},{"key":"6660_CR9","doi-asserted-by":"crossref","unstructured":"Hart P (1968) The condensed nearest neighbor rule (corresp.). IEEE Trans Inf Theory 14(3):515\u2013516","DOI":"10.1109\/TIT.1968.1054155"},{"key":"6660_CR10","doi-asserted-by":"publisher","first-page":"133653","DOI":"10.1109\/ACCESS.2019.2941229","volume":"7","author":"B Jang","year":"2019","unstructured":"Jang B, Kim M, Harerimana G et al (2019) Q-learning algorithms: A comprehensive classification and applications. IEEE access 7:133653\u2013133667","journal-title":"IEEE access"},{"key":"6660_CR11","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"6660_CR12","doi-asserted-by":"crossref","unstructured":"Li WZ, Wang CD, Xiong H et\u00a0al (2023) Graphsha: synthesizing harder samples for class-imbalanced node classification. arXiv:2306.09612","DOI":"10.1145\/3580305.3599374"},{"key":"6660_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127229","volume":"574","author":"X Li","year":"2024","unstructured":"Li X, Fan Z, Huang F et al (2024) Graph neural network with curriculum learning for imbalanced node classification. Neurocomputing 574:127229","journal-title":"Neurocomputing"},{"key":"6660_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1578-z","volume":"18","author":"L Ma","year":"2017","unstructured":"Ma L, Fan S (2017) Cure-smote algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests. BMC Bioinf 18:1\u201318","journal-title":"BMC Bioinf"},{"key":"6660_CR15","unstructured":"More A (2016) Survey of resampling techniques for improving classification performance in unbalanced datasets. arXiv:1608.06048"},{"key":"6660_CR16","unstructured":"Park J, Song J, Yang E (2022) Graphens: Neighbor-aware ego network synthesis for class-imbalanced node classification. In: The Tenth international conference on learning representations, ICLR 2022, International Conference on Learning Representations (ICLR)"},{"issue":"3","key":"6660_CR17","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(3):93\u201393","journal-title":"AI Mag"},{"key":"6660_CR18","doi-asserted-by":"crossref","unstructured":"Shen X, Zhu X, Jiang X et\u00a0al (2017) Visualization of non-metric relationships by adaptive learning multiple maps t-sne regularization. In: 2017 IEEE International conference on big data (Big Data), IEEE, pp 3882\u20133887","DOI":"10.1109\/BigData.2017.8258393"},{"key":"6660_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2021.775688","volume":"15","author":"S Shi","year":"2021","unstructured":"Shi S, Qiao K, Yang S et al (2021) Boosting-gnn: boosting algorithm for graph networks on imbalanced node classification. Front Neurorobotics 15:775688","journal-title":"Front Neurorobotics"},{"key":"6660_CR20","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.ins.2020.03.027","volume":"525","author":"Y Song","year":"2020","unstructured":"Song Y, Wang Y, Ye X et al (2020) Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in p2p lending. Inf Sci 525:182\u2013204","journal-title":"Inf Sci"},{"key":"6660_CR21","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et\u00a0al (2018) Graph attention networks. In: International conference on learning representations"},{"key":"6660_CR22","doi-asserted-by":"crossref","unstructured":"Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst, Man, Cybernet SMC-2(3):408\u2013421","DOI":"10.1109\/TSMC.1972.4309137"},{"key":"6660_CR23","unstructured":"Wu L, Lin H, Gao Z et\u00a0al (2021) Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv:2106.11133"},{"issue":"6","key":"6660_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-023-3897-2","volume":"67","author":"R Xia","year":"2024","unstructured":"Xia R, Zhang C, Zhang Y et al (2024) A novel graph oversampling framework for node classification in class-imbalanced graphs. Sci China Inf Sci 67(6):1\u201316","journal-title":"Sci China Inf Sci"},{"key":"6660_CR25","doi-asserted-by":"crossref","unstructured":"Xiao Z, Tong H (2025) Federated contrastive learning with feature-based distillation for human activity recognition. IEEE Trans Comput Soc Syst pp 1\u201314","DOI":"10.1109\/TCSS.2024.3510428"},{"issue":"4","key":"6660_CR26","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TCDS.2024.3370219","volume":"16","author":"Z Xiao","year":"2024","unstructured":"Xiao Z, Xu X, Xing H et al (2024) Dtcm: Deep transformer capsule mutual distillation for multivariate time series classification. IEEE Trans Cognit Develop Syst 16(4):1445\u20131461","journal-title":"IEEE Trans Cognit Develop Syst"},{"key":"6660_CR27","unstructured":"Xu K, Hu W, Leskovec J et\u00a0al (2018) How powerful are graph neural networks? arXiv:1810.00826"},{"issue":"01","key":"6660_CR28","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TKDE.2021.3072345","volume":"35","author":"Y Ye","year":"2023","unstructured":"Ye Y, Ji S (2023) Sparse graph attention networks. IEEE Trans Knowl Data Eng 35(01):905\u2013916","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6660_CR29","doi-asserted-by":"crossref","unstructured":"Yuan B, Ma X (2012) Sampling+ reweighting: Boosting the performance of adaboost on imbalanced datasets. In: The 2012 international joint conference on neural networks (IJCNN), IEEE, pp 1\u20136","DOI":"10.1109\/IJCNN.2012.6252738"},{"key":"6660_CR30","doi-asserted-by":"crossref","unstructured":"Zhao T, Zhang X, Wang S (2021) Graphsmote: Imbalanced node classification on graphs with graph neural networks. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 833\u2013841","DOI":"10.1145\/3437963.3441720"},{"key":"6660_CR31","doi-asserted-by":"crossref","unstructured":"Zhou M, Gong Z (2023) Graphsr: a data augmentation algorithm for imbalanced node classification. In: Proceedings of the AAAI Conference on artificial intelligence, pp 4954\u20134962","DOI":"10.1609\/aaai.v37i4.25622"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06660-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06660-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06660-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:57:21Z","timestamp":1758290241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06660-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":31,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["6660"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06660-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"17 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have read and agreed to the published version of the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The research was conducted without any commercial or financial conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"746"}}