{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T16:46:57Z","timestamp":1758041217814,"version":"3.44.0"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032045577"},{"type":"electronic","value":"9783032045584"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04558-4_29","type":"book-chapter","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:17:04Z","timestamp":1757589424000},"page":"361-378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Graph Neural Networks with\u00a0Mixup-Based Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Tianai","family":"Yue","sequence":"first","affiliation":[]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"29_CR1","unstructured":"Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bian, Y., Xiao, X., Rong, Y., Xu, T., Huang, J.: On self-distilling graph neural network. arXiv preprint arXiv:2011.02255 (2020)","DOI":"10.24963\/ijcai.2021\/314"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257\u2013266 (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Choi, H., Jeon, E.S., Shukla, A., Turaga, P.: Understanding the role of mixup in knowledge distillation: an empirical study. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2319\u20132328 (2023)","DOI":"10.1109\/WACV56688.2023.00235"},{"issue":"1","key":"29_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3610535","volume":"18","author":"A Chowdhury","year":"2023","unstructured":"Chowdhury, A., Srinivasan, S., Mukherjee, A., Bhowmick, S., Ghosh, K.: Improving node classification accuracy of GNN through input and output intervention. ACM Trans. Knowl. Discov. Data 18(1), 1\u201331 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Cook, D.J., Holder, L.B.: Mining Graph Data. Wiley (2006)","DOI":"10.1002\/0470073047"},{"key":"29_CR7","unstructured":"Errica, F., Podda, M., Bacciu, D., Micheli, A.: A fair comparison of graph neural networks for graph classification. arXiv preprint arXiv:1912.09893 (2019)"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Feng, K., Li, C., Yuan, Y., Wang, G.: FreeKD: free-direction knowledge distillation for graph neural networks. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 357\u2013366 (2022)","DOI":"10.1145\/3534678.3539320"},{"key":"29_CR9","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"29_CR10","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"29_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"29_CR12","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS \u201920, Red Hook, NY, USA. Curran Associates Inc. (2020)"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Kassam, S.A.: Signal Detection in Non-Gaussian noise. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4612-3834-8","DOI":"10.1007\/978-1-4612-3834-8"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, T., Shin, W.Y., Kim, S.W.: Monet: modality-embracing graph convolutional network and target-aware attention for multimedia recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 332\u2013340 (2024)","DOI":"10.1145\/3616855.3635817"},{"key":"29_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"29_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126441","volume":"549","author":"X Li","year":"2023","unstructured":"Li, X., Sun, L., Ling, M., Peng, Y.: A survey of graph neural network based recommendation in social networks. Neurocomputing 549, 126441 (2023)","journal-title":"Neurocomputing"},{"key":"29_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127229","volume":"574","author":"X Li","year":"2024","unstructured":"Li, X., et al.: Graph neural network with curriculum learning for imbalanced node classification. Neurocomputing 574, 127229 (2024)","journal-title":"Neurocomputing"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.neucom.2022.08.022","volume":"507","author":"J Liu","year":"2022","unstructured":"Liu, J., Zheng, T., Hao, Q.: Hire: distilling high-order relational knowledge from heterogeneous graph neural networks. Neurocomputing 507, 67\u201383 (2022)","journal-title":"Neurocomputing"},{"key":"29_CR19","unstructured":"Liu, J., Zheng, T., Zhang, G., Hao, Q.: Graph-based knowledge distillation: a survey and experimental evaluation. arXiv preprint arXiv:2302.14643 (2023)"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Lu, W., Guan, Z., Zhao, W., Yang, Y.: Adagmlp: Adaboosting gnn-to-mlp knowledge distillation. arXiv:abs\/2405.14307 (2024). https:\/\/api.semanticscholar.org\/CorpusID:269983237","DOI":"10.1145\/3637528.3671699"},{"issue":"4","key":"29_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3012704","volume":"49","author":"V Mart\u00ednez","year":"2016","unstructured":"Mart\u00ednez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. (CSUR) 49(4), 1\u201333 (2016)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"29_CR22","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024\u20138035"},{"issue":"1","key":"29_CR23","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Networks"},{"issue":"10","key":"29_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3661821","volume":"56","author":"K Sharma","year":"2024","unstructured":"Sharma, K., et al.: A survey of graph neural networks for social recommender systems. ACM Comput. Surv. 56(10), 1\u201334 (2024)","journal-title":"ACM Comput. Surv."},{"key":"29_CR25","unstructured":"Tian, Y., Pei, S., Zhang, X., Zhang, C., Chawla, N.V.: Knowledge distillation on graphs: a survey. arXiv preprint arXiv:2302.00219 (2023)"},{"key":"29_CR26","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations"},{"issue":"11","key":"29_CR27","doi-asserted-by":"publisher","first-page":"10981","DOI":"10.1109\/TKDE.2022.3233481","volume":"35","author":"H Wang","year":"2023","unstructured":"Wang, H., Cui, Z., Liu, R., Fang, L., Sha, Y.: A multi-type transferable method for missing link prediction in heterogeneous social networks. IEEE Trans. Knowl. Data Eng. 35(11), 10981\u201310991 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"29_CR28","unstructured":"Wang, M.Y.: Deep graph library: Towards efficient and scalable deep learning on graphs. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"issue":"1","key":"29_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3584945","volume":"42","author":"L Wei","year":"2023","unstructured":"Wei, L., Zhao, H., He, Z., Yao, Q.: Neural architecture search for GNN-based graph classification. ACM Trans. Inf. Syst. 42(1), 1\u201329 (2023)","journal-title":"ACM Trans. Inf. Syst."},{"key":"29_CR30","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Yan, B., Wang, C., Guo, G., Lou, Y.: TinyGNN: learning efficient graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1848\u20131856 (2020)","DOI":"10.1145\/3394486.3403236"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Yang, C., Liu, J., Shi, C.: Extract the knowledge of graph neural networks and go beyond it: an effective knowledge distillation framework. In: Proceedings of the Web Conference 2021, pp. 1227\u20131237 (2021)","DOI":"10.1145\/3442381.3450068"},{"key":"29_CR33","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1007\/978-3-031-20053-3_31","volume-title":"ECCV 2022","author":"C Yang","year":"2022","unstructured":"Yang, C., et al.: MixSKD: self-knowledge distillation from mixup for image recognition. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13684, pp. 534\u2013551. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_31"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.: Distilling knowledge from graph convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7074\u20137083 (2020)","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"29_CR35","unstructured":"Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: International Conference on Machine Learning, pp. 40\u201348. PMLR (2016)"},{"key":"29_CR36","unstructured":"Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.: Graphsaint: graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931 (2019)"},{"key":"29_CR37","unstructured":"Zhang, C., et al.: When sparsity meets contrastive models: less graph data can bring better class-balanced representations. In: International Conference on Machine Learning, pp. 41133\u201341150. PMLR (2023)"},{"key":"29_CR38","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"29_CR39","unstructured":"Zhang, S., Liu, Y., Sun, Y., Shah, N.: Graph-less neural networks: teaching old MLPs new tricks via distillation. In: International Conference on Learning Representations"},{"key":"29_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, W., Miao, X., Shao, Y., Jiang, J., Chen, L., Ruas, O., Cui, B.: Reliable data distillation on graph convolutional network. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1399\u20131414 (2020)","DOI":"10.1145\/3318464.3389706"},{"key":"29_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.690049","volume":"12","author":"XM Zhang","year":"2021","unstructured":"Zhang, X.M., Liang, L., Liu, L., Tang, M.J.: Graph neural networks and their current applications in bioinformatics. Front. Genet. 12, 690049 (2021)","journal-title":"Front. Genet."},{"key":"29_CR42","unstructured":"Zheng, W., Huang, E.W., Rao, N., Katariya, S., Wang, Z., Subbian, K.: Cold brew: distilling graph node representations with incomplete or missing neighborhoods. arXiv preprint arXiv:2111.04840 (2021)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04558-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:17:18Z","timestamp":1757589438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04558-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032045577","9783032045584"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04558-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}