{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:27:59Z","timestamp":1782185279147,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["1947135,2134079,1939725"],"award-info":[{"award-number":["1947135,2134079,1939725"]}]},{"name":"DARPA","award":["HR001121C0165"],"award-info":[{"award-number":["HR001121C0165"]}]},{"name":"ARO","award":["W911NF2110088"],"award-info":[{"award-number":["W911NF2110088"]}]},{"name":"DHS","award":["17STQAC00001-06-00"],"award-info":[{"award-number":["17STQAC00001-06-00"]}]},{"name":"NIFA","award":["2020-67021-32799"],"award-info":[{"award-number":["2020-67021-32799"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599398","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"2850-2861","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Kernel Ridge Regression-Based Graph Dataset Distillation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6675-1398","authenticated-orcid":false,"given":"Zhe","family":"Xu","sequence":"first","affiliation":[{"name":"University of Illinois Urbana-Champaign, Champaign, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5863-0588","authenticated-orcid":false,"given":"Yuzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8390-7147","authenticated-orcid":false,"given":"Menghai","family":"Pan","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6360-558X","authenticated-orcid":false,"given":"Huiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1714-1996","authenticated-orcid":false,"given":"Mahashweta","family":"Das","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3020-9828","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4405-3887","authenticated-orcid":false,"given":"Hanghang","family":"Tong","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Champaign, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Muhammad Fatih Balin, and James Zou","author":"Abid Abubakar","year":"2019","unstructured":"Abubakar Abid , Muhammad Fatih Balin, and James Zou . 2019 . Concrete autoencoders for differentiable feature selection and reconstruction. arXiv preprint arXiv:1901.09346 (2019). Abubakar Abid, Muhammad Fatih Balin, and James Zou. 2019. Concrete autoencoders for differentiable feature selection and reconstruction. arXiv preprint arXiv:1901.09346 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR, 242--252","author":"Allen-Zhu Zeyuan","year":"2019","unstructured":"Zeyuan Allen-Zhu , Yuanzhi Li , and Zhao Song . 2019 . A convergence theory for deep learning via over-parameterization . In International Conference on Machine Learning. PMLR, 242--252 . Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 2019. A convergence theory for deep learning via over-parameterization. In International Conference on Machine Learning. PMLR, 242--252."},{"key":"e_1_3_2_2_3_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Arora Sanjeev","year":"2019","unstructured":"Sanjeev Arora , Simon S Du , Wei Hu , Zhiyuan Li , Russ R Salakhutdinov , and Ruosong Wang . 2019 . On exact computation with an infinitely wide neural net . Advances in Neural Information Processing Systems , Vol. 32 (2019). Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang. 2019. On exact computation with an infinitely wide neural net. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_4_1","volume-title":"Graph Coarsening with Neural Networks. In International Conference on Learning Representations.","author":"Cai Chen","year":"2021","unstructured":"Chen Cai , Dingkang Wang , and Yusu Wang . 2021 . Graph Coarsening with Neural Networks. In International Conference on Learning Representations. Chen Cai, Dingkang Wang, and Yusu Wang. 2021. Graph Coarsening with Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750--4759","author":"Cazenavette George","year":"2022","unstructured":"George Cazenavette , Tongzhou Wang , Antonio Torralba , Alexei A Efros , and Jun-Yan Zhu . 2022 . Dataset distillation by matching training trajectories . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750--4759 . George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022. Dataset distillation by matching training trajectories. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750--4759."},{"key":"e_1_3_2_2_6_1","volume-title":"Bilevel programming: A survey. 4or","author":"Colson Beno\u00eet","year":"2005","unstructured":"Beno\u00eet Colson , Patrice Marcotte , and Gilles Savard . 2005. Bilevel programming: A survey. 4or , Vol. 3 , 2 ( 2005 ), 87--107. Beno\u00eet Colson, Patrice Marcotte, and Gilles Savard. 2005. Bilevel programming: A survey. 4or, Vol. 3, 2 (2005), 87--107."},{"key":"e_1_3_2_2_7_1","volume-title":"Data augmentation for deep graph learning: A survey. arXiv preprint arXiv:2202.08235","author":"Ding Kaize","year":"2022","unstructured":"Kaize Ding , Zhe Xu , Hanghang Tong , and Huan Liu . 2022. Data augmentation for deep graph learning: A survey. arXiv preprint arXiv:2202.08235 ( 2022 ). Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu. 2022. Data augmentation for deep graph learning: A survey. arXiv preprint arXiv:2202.08235 (2022)."},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, 1675--1685","author":"Du Simon","year":"2019","unstructured":"Simon Du , Jason Lee , Haochuan Li , Liwei Wang , and Xiyu Zhai . 2019 b. Gradient descent finds global minima of deep neural networks . In International conference on machine learning. PMLR, 1675--1685 . Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai. 2019b. Gradient descent finds global minima of deep neural networks. In International conference on machine learning. PMLR, 1675--1685."},{"key":"e_1_3_2_2_9_1","volume-title":"Graph neural tangent kernel: Fusing graph neural networks with graph kernels. Advances in neural information processing systems","author":"Du Simon S","year":"2019","unstructured":"Simon S Du , Kangcheng Hou , Russ R Salakhutdinov , Barnabas Poczos , Ruosong Wang , and Keyulu Xu. 2019a. Graph neural tangent kernel: Fusing graph neural networks with graph kernels. Advances in neural information processing systems , Vol. 32 ( 2019 ). Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu. 2019a. Graph neural tangent kernel: Fusing graph neural networks with graph kernels. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371789"},{"key":"e_1_3_2_2_11_1","volume-title":"Facility location: concepts, models, algorithms and case studies","author":"Farahani Reza Zanjirani","unstructured":"Reza Zanjirani Farahani and Masoud Hekmatfar . 2009. Facility location: concepts, models, algorithms and case studies . Springer Science & Business Media . Reza Zanjirani Farahani and Masoud Hekmatfar. 2009. Facility location: concepts, models, algorithms and case studies. Springer Science & Business Media."},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Machine Learning. PMLR, 1165--1173","author":"Franceschi Luca","year":"2017","unstructured":"Luca Franceschi , Michele Donini , Paolo Frasconi , and Massimiliano Pontil . 2017 . Forward and reverse gradient-based hyperparameter optimization . In International Conference on Machine Learning. PMLR, 1165--1173 . Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil. 2017. Forward and reverse gradient-based hyperparameter optimization. In International Conference on Machine Learning. PMLR, 1165--1173."},{"key":"e_1_3_2_2_13_1","volume-title":"International Conference on Machine Learning. PMLR, 1568--1577","author":"Franceschi Luca","year":"2018","unstructured":"Luca Franceschi , Paolo Frasconi , Saverio Salzo , Riccardo Grazzi , and Massimiliano Pontil . 2018 . Bilevel programming for hyperparameter optimization and meta-learning . In International Conference on Machine Learning. PMLR, 1568--1577 . Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. 2018. Bilevel programming for hyperparameter optimization and meta-learning. In International Conference on Machine Learning. PMLR, 1568--1577."},{"key":"e_1_3_2_2_14_1","volume-title":"International conference on machine learning. PMLR","author":"Franceschi Luca","year":"2019","unstructured":"Luca Franceschi , Mathias Niepert , Massimiliano Pontil , and Xiao He . 2019 . Learning discrete structures for graph neural networks . In International conference on machine learning. PMLR , 1972--1982. Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019. Learning discrete structures for graph neural networks. In International conference on machine learning. PMLR, 1972--1982."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2022.1062637"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3572726"},{"key":"e_1_3_2_2_17_1","volume-title":"Cognitron: A self-organizing multilayered neural network. Biological cybernetics","author":"Fukushima Kunihiko","year":"1975","unstructured":"Kunihiko Fukushima . 1975 . Cognitron: A self-organizing multilayered neural network. Biological cybernetics , Vol. 20 , 3 (1975), 121--136. Kunihiko Fukushima. 1975. Cognitron: A self-organizing multilayered neural network. Biological cybernetics, Vol. 20, 3 (1975), 121--136."},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Learning Representations.","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In International Conference on Learning Representations. Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","volume-title":"Clustering to minimize the maximum intercluster distance. Theoretical computer science","author":"Gonzalez Teofilo F","year":"1985","unstructured":"Teofilo F Gonzalez . 1985. Clustering to minimize the maximum intercluster distance. Theoretical computer science , Vol. 38 ( 1985 ), 293--306. Teofilo F Gonzalez. 1985. Clustering to minimize the maximum intercluster distance. Theoretical computer science, Vol. 38 (1985), 293--306."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939747"},{"key":"e_1_3_2_2_21_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Matthias Fey , Marinka Zitnik , Yuxiao Dong , Hongyu Ren , Bowen Liu , Michele Catasta , and Jure Leskovec . 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems , Vol. 33 ( 2020 ), 22118--22133. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22118--22133."},{"key":"e_1_3_2_2_22_1","volume-title":"Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems","author":"Jacot Arthur","year":"2018","unstructured":"Arthur Jacot , Franck Gabriel , and Cl\u00e9ment Hongler . 2018. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems , Vol. 31 ( 2018 ). Arthur Jacot, Franck Gabriel, and Cl\u00e9ment Hongler. 2018. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_23_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Jang Eric","year":"2017","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2017. Categorical Reparameterization with Gumbel-Softmax . In 5th International Conference on Learning Representations, ICLR 2017 , Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=rkE3y85ee Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rkE3y85ee"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00479-8"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"e_1_3_2_2_27_1","volume-title":"Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations.","author":"Jin Wei","year":"2022","unstructured":"Wei Jin , Lingxiao Zhao , Shichang Zhang , Yozen Liu , Jiliang Tang , and Neil Shah . 2022 b. Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations. Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. 2022b. Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972825.71"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the 20th international conference on machine learning (ICML-03)","author":"Kashima Hisashi","year":"2003","unstructured":"Hisashi Kashima , Koji Tsuda , and Akihiro Inokuchi . 2003 . Marginalized kernels between labeled graphs . In Proceedings of the 20th international conference on machine learning (ICML-03) . 321--328. Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi. 2003. Marginalized kernels between labeled graphs. In Proceedings of the 20th international conference on machine learning (ICML-03). 321--328."},{"key":"e_1_3_2_2_30_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_31_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=SJU4ayYgl Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Learning Representations.","author":"Lee Jaehoon","year":"2018","unstructured":"Jaehoon Lee , Yasaman Bahri , Roman Novak , Samuel S Schoenholz , Jeffrey Pennington , and Jascha Sohl-Dickstein . 2018 . Deep Neural Networks as Gaussian Processes . In International Conference on Learning Representations. Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein. 2018. Deep Neural Networks as Gaussian Processes. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939768"},{"key":"e_1_3_2_2_34_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 1540--1552","author":"Lorraine Jonathan","year":"2020","unstructured":"Jonathan Lorraine , Paul Vicol , and David Duvenaud . 2020 . Optimizing millions of hyperparameters by implicit differentiation . In International Conference on Artificial Intelligence and Statistics. PMLR, 1540--1552 . Jonathan Lorraine, Paul Vicol, and David Duvenaud. 2020. Optimizing millions of hyperparameters by implicit differentiation. In International Conference on Artificial Intelligence and Statistics. PMLR, 1540--1552."},{"key":"e_1_3_2_2_35_1","volume-title":"International conference on machine learning. PMLR, 2113--2122","author":"Maclaurin Dougal","year":"2015","unstructured":"Dougal Maclaurin , David Duvenaud , and Ryan Adams . 2015 . Gradient-based hyperparameter optimization through reversible learning . In International conference on machine learning. PMLR, 2113--2122 . Dougal Maclaurin, David Duvenaud, and Ryan Adams. 2015. Gradient-based hyperparameter optimization through reversible learning. In International conference on machine learning. PMLR, 2113--2122."},{"key":"e_1_3_2_2_36_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Maddison Chris J.","year":"2017","unstructured":"Chris J. Maddison , Andriy Mnih , and Yee Whye Teh . 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables . In 5th International Conference on Learning Representations, ICLR 2017 , Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=S1jE5L5gl Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=S1jE5L5gl"},{"key":"e_1_3_2_2_37_1","volume-title":"ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL 2020","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris , Nils M. Kriege , Franka Bause , Kristian Kersting , Petra Mutzel , and Marion Neumann . 2020 . TUDataset: A collection of benchmark datasets for learning with graphs . In ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL 2020 ). arxiv: 2007.08663 www.graphlearning.io Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020. TUDataset: A collection of benchmark datasets for learning with graphs. In ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL 2020). arxiv: 2007.08663 www.graphlearning.io"},{"key":"e_1_3_2_2_38_1","volume-title":"Bayesian Learning for Neural Networks","author":"Neal Radford M","unstructured":"Radford M Neal . 1996. Priors for infinite networks . In Bayesian Learning for Neural Networks . Springer , 29-53. Radford M Neal. 1996. Priors for infinite networks. In Bayesian Learning for Neural Networks. Springer, 29-53."},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Learning Representations.","author":"Nguyen Timothy","year":"2021","unstructured":"Timothy Nguyen , Zhourong Chen , and Jaehoon Lee . 2021 . Dataset Meta-Learning from Kernel Ridge-Regression . In International Conference on Learning Representations. Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. 2021. Dataset Meta-Learning from Kernel Ridge-Regression. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20767"},{"key":"e_1_3_2_2_41_1","volume-title":"International conference on machine learning. PMLR, 737--746","author":"Pedregosa Fabian","year":"2016","unstructured":"Fabian Pedregosa . 2016 . Hyperparameter optimization with approximate gradient . In International conference on machine learning. PMLR, 737--746 . Fabian Pedregosa. 2016. Hyperparameter optimization with approximate gradient. In International conference on machine learning. PMLR, 737--746."},{"key":"e_1_3_2_2_42_1","volume-title":"Handbook of discrete and computational geometry","author":"Phillips Jeff M","unstructured":"Jeff M Phillips . 2017. Coresets and sketches . In Handbook of discrete and computational geometry . Chapman and Hall\/CRC , 1269--1288. Jeff M Phillips. 2017. Coresets and sketches. In Handbook of discrete and computational geometry. Chapman and Hall\/CRC, 1269--1288."},{"key":"e_1_3_2_2_43_1","volume-title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations.","author":"Sener Ozan","year":"2018","unstructured":"Ozan Sener and Silvio Savarese . 2018 . Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations. Ozan Sener and Silvio Savarese. 2018. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_44_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1723--1732","author":"Shaban Amirreza","year":"2019","unstructured":"Amirreza Shaban , Ching-An Cheng , Nathan Hatch , and Byron Boots . 2019 . Truncated back-propagation for bilevel optimization . In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1723--1732 . Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots. 2019. Truncated back-propagation for bilevel optimization. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1723--1732."},{"key":"e_1_3_2_2_45_1","first-page":"15920","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume":"34","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh , Pan Li , Cong Hao , and Jennifer Neville . 2021 . Adversarial graph augmentation to improve graph contrastive learning . Advances in Neural Information Processing Systems , Vol. 34 (2021), 15920 -- 15933 . Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15920--15933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_46_1","volume-title":"Dataset distillation. arXiv preprint arXiv:1811.10959","author":"Wang Tongzhou","year":"2018","unstructured":"Tongzhou Wang , Jun-Yan Zhu , Antonio Torralba , and Alexei A Efros . 2018. Dataset distillation. arXiv preprint arXiv:1811.10959 ( 2018 ). Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018. Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"e_1_3_2_2_48_1","volume-title":"International conference on machine learning. PMLR, 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 . Simplifying graph convolutional networks . In International conference on machine learning. PMLR, 6861--6871 . Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861--6871."},{"key":"e_1_3_2_2_49_1","volume-title":"Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133","author":"Wu Lirong","year":"2021","unstructured":"Lirong Wu , Haitao Lin , Zhangyang Gao , Cheng Tan , Stan Li , 2021 . Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133 (2021). Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan Li, et al. 2021. Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133 (2021)."},{"key":"e_1_3_2_2_50_1","volume-title":"International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In International Conference on Learning Representations. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_51_1","volume-title":"Generalized Few-Shot Node Classification. In IEEE International Conference on Data Mining, ICDM 2022","author":"Xu Zhe","year":"2022","unstructured":"Zhe Xu , Kaize Ding , Yu-Xiong Wang , Huan Liu , and Hanghang Tong . 2022 a. Generalized Few-Shot Node Classification. In IEEE International Conference on Data Mining, ICDM 2022 , Orlando, FL, USA, November 28 - Dec. 1, 2022, Xingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, and Xindong Wu (Eds.). IEEE, 608--617. https:\/\/doi.org\/10.1109\/ICDM54844.2022.00071 10.1109\/ICDM54844.2022.00071 Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, and Hanghang Tong. 2022a. Generalized Few-Shot Node Classification. In IEEE International Conference on Data Mining, ICDM 2022, Orlando, FL, USA, November 28 - Dec. 1, 2022, Xingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, and Xindong Wu (Eds.). IEEE, 608--617. https:\/\/doi.org\/10.1109\/ICDM54844.2022.00071"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512180"},{"key":"e_1_3_2_2_53_1","volume-title":"International Conference on Machine Learning. PMLR, 12121--12132","author":"You Yuning","year":"2021","unstructured":"Yuning You , Tianlong Chen , Yang Shen , and Zhangyang Wang . 2021 . Graph contrastive learning automated . In International Conference on Machine Learning. PMLR, 12121--12132 . Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In International Conference on Machine Learning. PMLR, 12121--12132."},{"key":"e_1_3_2_2_54_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You , Tianlong Chen , Yongduo Sui , Ting Chen , Zhangyang Wang , and Yang Shen . 2020 . Graph contrastive learning with augmentations . Advances in Neural Information Processing Systems , Vol. 33 (2020), 5812 -- 5823 . Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5812--5823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.64"},{"key":"e_1_3_2_2_56_1","volume-title":"Learning Optimal Propagation for Graph Neural Networks. arXiv preprint arXiv:2205.02998","author":"Zhao Beidi","year":"2022","unstructured":"Beidi Zhao , Boxin Du , Zhe Xu , Liangyue Li , and Hanghang Tong . 2022a. Learning Optimal Propagation for Graph Neural Networks. arXiv preprint arXiv:2205.02998 ( 2022 ). Beidi Zhao, Boxin Du, Zhe Xu, Liangyue Li, and Hanghang Tong. 2022a. Learning Optimal Propagation for Graph Neural Networks. arXiv preprint arXiv:2205.02998 (2022)."},{"key":"e_1_3_2_2_57_1","first-page":"3","article-title":"Dataset Condensation with Gradient Matching","volume":"1","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao , Konda Reddy Mopuri , and Hakan Bilen . 2021 b. Dataset Condensation with Gradient Matching . ICLR , Vol. 1 , 2 (2021), 3 . Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021b. Dataset Condensation with Gradient Matching. ICLR, Vol. 1, 2 (2021), 3.","journal-title":"ICLR"},{"key":"e_1_3_2_2_58_1","volume-title":"Graph Data Augmentation for Graph Machine Learning: A Survey. arXiv preprint arXiv:2202.08871","author":"Zhao Tong","year":"2022","unstructured":"Tong Zhao , Gang Liu , Stephan G\u00fcnnemann , and Meng Jiang . 2022b. Graph Data Augmentation for Graph Machine Learning: A Survey. arXiv preprint arXiv:2202.08871 ( 2022 ). Tong Zhao, Gang Liu, Stephan G\u00fcnnemann, and Meng Jiang. 2022b. Graph Data Augmentation for Graph Machine Learning: A Survey. arXiv preprint arXiv:2202.08871 (2022)."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599398","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599398","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599398","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:36Z","timestamp":1750178256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":61,"alternative-id":["10.1145\/3580305.3599398","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599398","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}