{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T09:45:33Z","timestamp":1772271933858,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703430","type":"print"},{"value":"9783031703447","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70344-7_4","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:02:43Z","timestamp":1724918563000},"page":"53-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Simple Graph Condensation"],"prefix":"10.1007","author":[{"given":"Zhenbang","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shunyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Huiqiong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[]},{"given":"Tongya","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"4_CR1","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? In: ICLR (2021)"},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, D., Wang, X., Zhang, Y., Zhang, L.: Graph neural network for fraud detection via spatial-temporal attention. TKDE, 3800\u20133813 (2020)","DOI":"10.1109\/TKDE.2020.3025588"},{"key":"4_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: SIGKDD, pp. 257\u2013266 (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"4_CR4","unstructured":"Deng, Z., Russakovsky, O.: Remember the past: Distilling datasets into addressable memories for neural networks. arXiv preprint arXiv:2206.02916 (2022)"},{"key":"4_CR5","unstructured":"Dong, T., Zhao, B., Lyu, L.: Privacy for free: How does dataset condensation help privacy? In: ICML, pp. 5378\u20135396 (2022)"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Fan, W., et al.: Graph neural networks for social recommendation. In: WWW, pp. 417\u2013426 (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"4_CR7","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS (2017)"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639\u2013648 (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"4_CR9","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: NeurIPS, pp. 22118\u201322133 (2020)"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Jin, W., et al.: Condensing graphs via one-step gradient matching. In: SIGKDD, pp. 720\u2013730 (2022)","DOI":"10.1145\/3534678.3539429"},{"key":"4_CR11","unstructured":"Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J., Shah, N.: Graph condensation for graph neural networks. In: ICLR (2022)"},{"key":"4_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"4_CR13","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":"4_CR14","unstructured":"Liu, M., Li, S., Chen, X., Song, L.: Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697 (2022)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Transmission interface power flow adjustment: a deep reinforcement learning approach based on multi-task attribution map. IEEE Trans. Power Syst. 3324\u20133335 (2024)","DOI":"10.1109\/TPWRS.2023.3298007"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhou, Y., Song, M., Bu, G., Guo, J., Chen, C.: Progressive decision-making framework for power system topology control. Expert Syst. Appl. 121070 (2024)","DOI":"10.1016\/j.eswa.2023.121070"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Liu, S., Ye, J., Yu, R., Wang, X.: Slimmable dataset condensation. In: CVPR, pp. 3759\u20133768 (2023)","DOI":"10.1109\/CVPR52729.2023.00366"},{"key":"4_CR18","unstructured":"Loukas, A.: Graph reduction with spectral and cut guarantees. J. Mach. Learn. Res. 1\u201342 (2019)"},{"key":"4_CR19","unstructured":"Loukas, A., Vandergheynst, P.: Spectrally approximating large graphs with smaller graphs. In: ICML (2018)"},{"key":"4_CR20","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: ICLR (2021)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Peleg, D., Sch\u00e4ffer, A.A.: Graph spanners. J. Graph Theory (1989)","DOI":"10.1002\/jgt.3190130114"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Peng, J., Chen, Z., Shao, Y., Shen, Y., Chen, L., Cao, J.: Sancus: staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks. VLDB, 1937\u20131950 (2022)","DOI":"10.14778\/3538598.3538614"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Reiser, P., et\u00a0al.: Graph neural networks for materials science and chemistry. Commun. Mater.\u00a093 (2022)","DOI":"10.1038\/s43246-022-00315-6"},{"key":"4_CR24","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: ICLR (2018)"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Spielman, D.A., Teng, S.H.: Spectral sparsification of graphs. SIAM J. Comput. 981\u20131025 (2011)","DOI":"10.1137\/08074489X"},{"key":"4_CR26","unstructured":"Such, F.P., Rawal, A., Lehman, J., Stanley, K., Clune, J.: Generative teaching networks: accelerating neural architecture search by learning to generate synthetic training data. In: ICML, pp. 9206\u20139216 (2020)"},{"key":"4_CR27","unstructured":"Tolstikhin, I.O., et\u00a0al.: MLP-mixer: an all-MLP architecture for vision. In: NeurIPS, pp. 24261\u201324272 (2021)"},{"key":"4_CR28","unstructured":"Wan, C., Li, Y., Wolfe, C.R., Kyrillidis, A., Kim, N.S., Lin, Y.: PipeGCN: efficient full-graph training of graph convolutional networks with pipelined feature communication. In: ICLR (2021)"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: Cafe: learning to condense dataset by aligning features. In: CVPR, pp. 12196\u201312205 (2022)","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"4_CR30","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint (2018)"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Welling, M.: Herding dynamical weights to learn. In: ICML, pp. 1121\u20131128 (2009)","DOI":"10.1145\/1553374.1553517"},{"key":"4_CR32","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: ICML, pp. 6861\u20136871 (2019)"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L.: Adversarial examples on graph data: deep insights into attack and defense. arXiv preprint arXiv:1903.01610 (2019)","DOI":"10.24963\/ijcai.2019\/669"},{"key":"4_CR34","unstructured":"Wu, Q., et al.: SGFormer: simplifying and empowering transformers for large-graph representations. In: NeurIPS (2023)"},{"key":"4_CR35","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pi, D., Chen, J., Xie, M., Cao, J.: Rumor detection based on propagation graph neural network with attention mechanism. Expert Syst. Appl. 113595 (2020)","DOI":"10.1016\/j.eswa.2020.113595"},{"key":"4_CR36","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. TNNLS, 4\u201324 (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"4_CR37","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2018)"},{"key":"4_CR38","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.I., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: ICML, pp. 5453\u20135462 (2018)"},{"key":"4_CR39","unstructured":"Yang, B., et al.: Does graph distillation see like vision dataset counterpart? In: NeurIPS (2023)"},{"key":"4_CR40","unstructured":"Ying, C., et al.: Do transformers really perform badly for graph representation? In: NeurIPS, pp. 28877\u201328888 (2021)"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: SIGKDD, pp. 974\u2013983 (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"4_CR42","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: AAAI (2020)"},{"key":"4_CR43","unstructured":"Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.K.: Graphsaint: graph sampling based inductive learning method. In: ICLR (2020)"},{"key":"4_CR44","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NeurIPS (2018)"},{"key":"4_CR45","doi-asserted-by":"crossref","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with distribution matching. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6514\u20136523 (2023)","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"4_CR46","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: ICLR (2021)"},{"key":"4_CR47","unstructured":"Zheng, X., Zhang, M., Chen, C., Nguyen, Q.V.H., Zhu, X., Pan, S.: Structure-free graph condensation: From large-scale graphs to condensed graph-free data. In: NeurIPS (2023)"},{"key":"4_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open (2020)","DOI":"10.1016\/j.aiopen.2021.01.001"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70344-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:04:00Z","timestamp":1724918640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70344-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703430","9783031703447"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70344-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}