{"type":"ScholarlyArticle","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","authors":[{"name":"Mohammad Hashemi","slug":"mohammad-hashemi","given":"Mohammad","family":"Hashemi","affiliation":"Emory University ,"},{"name":"Shengbo Gong","slug":"shengbo-gong","given":"Shengbo","family":"Gong","affiliation":"Emory University ,"},{"name":"Juntong Ni","slug":"juntong-ni","given":"Juntong","family":"Ni","affiliation":"Emory University ,"},{"name":"Wenqi Fan","slug":"wenqi-fan","given":"Wenqi","family":"Fan","affiliation":"The Hong Kong Polytechnic University ,"},{"name":"B. Aditya Prakash","slug":"b-aditya-prakash","given":"B. Aditya","family":"Prakash","affiliation":"Georgia Institute of Technology"},{"name":"Wei Jin","slug":"wei-jin","given":"Wei","family":"Jin","affiliation":"Emory University ,"}],"abstract":"Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction techniques have gained prominence for simplifying large graphs while preserving essential properties. In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. Specifically, we establish a unified definition for these methods and introduce a hierarchical taxonomy to categorize the challenges they address. Our survey then systematically reviews the technical details of these methods and emphasizes their practical applications across diverse scenarios. Furthermore, we outline critical research directions to ensure the continued effectiveness of graph reduction techniques.","doi":"10.24963/ijcai.2024/891","url":"https://ijcai.org/proceedings/2024/891","pdf":"https://ijcai.org/proceedings/2024/0891.pdf","markdown":"https://ijcai.org/proceedings/2024/891.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/891","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":891,"pages":{"first":8058,"last":8066},"keywords":["Machine Learning: ML: Sequence and graph learning","Data Mining: DM: Mining graphs","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Althöfer et al., 1993] Ingo Althöfer, Gautam Das, David Dobkin, Deborah Joseph, and José Soares. On sparse span- ners of weighted graphs. Discrete & Computational Ge- ometry, 1993.","title":"On sparse spanners of weighted graphs","authors":"Ingo Althöfer, Gautam Das, David Dobkin, Deborah Joseph, José Soares","year":"1993","venue":"Discrete & Computational Geometry","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Amiri et al., 2018] Sorour E Amiri, Bijaya Adhikari, Aditya Bharadwaj, and B Aditya Prakash. Netgist: Learning to generate task-based network summaries. In ICDM, 2018.","title":"Netgist: Learning to generate task-based network summaries","authors":"Bijaya Sorour E Amiri, Aditya Adhikari, Aditya Bharadwaj, Prakash","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Baswana and Sen, 2003] Surender Baswana and Sandeep Sen. A simple linear time algorithm for computing a (2 k-1)-spanner of o (n 1+ 1/k) size in weighted graphs. In ICALP, 2003.","title":"A simple linear time algorithm for computing a (2 k-1)-spanner of o (n 1+ 1/k) size in weighted graphs","authors":"Surender Baswana, Sandeep Sen","year":"2003","venue":"ICALP","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Batson et al., 2009] Joshua D Batson, Daniel A Spielman, and Nikhil Srivastava. Twice-ramanujan sparsifiers. In STOC, 2009.","title":"Twice-ramanujan sparsifiers","authors":"Joshua D Batson, Daniel A Spielman, Nikhil Srivastava","year":"2009","venue":"STOC","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Beg et al., 2018] Maham Anwar Beg, Muhammad Ahmad, Arif Zaman, and Imdadullah Khan. Scalable approxima- tion algorithm for graph summarization. In PAKDD, 2018.","title":"Scalable approximation algorithm for graph summarization","authors":"Maham Anwar Beg, Muhammad Ahmad, Arif Zaman, Imdadullah Khan","year":"2018","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen and Safro, 2011] Jie Chen and Ilya Safro. Algebraic distance on graphs. SIAM Journal on Scientific Comput- ing, 2011.","title":"Algebraic distance on graphs","authors":"Chen, Safro ;, Jie Chen, Ilya Safro","year":"2011","venue":"SIAM Journal on Scientific Computing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2018] Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. Harp: Hierarchical representation learning for networks. In AAAI, 2018.","title":"Harp: Hierarchical representation learning for networks","authors":"Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2021] Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. A unified lot- tery ticket hypothesis for graph neural networks. In ICML, 2021.","title":"A unified lottery ticket hypothesis for graph neural networks","authors":"Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dhillon et al., 2007] Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis. Weighted graph cuts without eigenvectors a multilevel approach. TPAMI, 2007.","title":"Weighted graph cuts without eigenvectors a multilevel approach","authors":"Yuqiang Inderjit S Dhillon, Brian Guan, Kulis","year":"2007","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Dong et al., 2022] Tian Dong, Bo Zhao, and Lingjuan Lyu. Privacy for free: How does dataset condensation help pri- vacy? In ICML, 2022.","title":"Privacy for free: How does dataset condensation help privacy?","authors":"Tian Dong, Bo Zhao, Lingjuan Lyu","year":"2022","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fang et al., 2010] Haw-ren Fang, Sophia Sakellaridi, and Yousef Saad. Multilevel manifold learning with applica- tion to spectral clustering. In CIKM, 2010.","title":"Multilevel manifold learning with application to spectral clustering","authors":"Fang","year":"2010","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fang et al., 2024] Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, and Xiangnan He. Exgc: Bridging efficiency and explainabil- ity in graph condensation. In WWW, 2024.","title":"Exgc: Bridging efficiency and explainability in graph condensation","authors":"Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He","year":"2024","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Feng et al., 2023] Qizhang Feng, Zhimeng Jiang, Ruiquan Li, Yicheng Wang, Na Zou, Jiang Bian, and Xia Hu. Fair graph distillation. In NeurIPS, 2023.","title":"Fair graph distillation","authors":"Feng","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Feng, 2016] Zhuo Feng. Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturba- tion analysis. In DAC, 2016.","title":"Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturbation analysis","authors":"Zhuo Feng","year":"2016","venue":"DAC","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Gao and Wu, 2023] Jian Gao and Jianshe Wu. Multiple sparse graphs condensation. Knowledge-Based Systems, 2023.","title":"Multiple sparse graphs condensation","authors":"Wu ; ] Jian Gao, Jianshe Gao, Wu","year":"2023","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Gupta et al., 2023] Mridul Gupta, Sahil Manchanda, Sayan Ranu, and Hariprasad Kodamana. Mirage: Model- agnostic graph distillation for graph classification. arXiv, 2023.","title":"Mirage: Modelagnostic graph distillation for graph classification","authors":"Mridul Gupta, Sahil Manchanda, Sayan Ranu, Hariprasad Kodamana","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hu and Lau, 2013] Pili Hu and Wing Cheong Lau. A survey and taxonomy of graph sampling. arXiv, 2013.","title":"A survey and taxonomy of graph sampling","authors":"Lau ; ] Pili Hu, Wing Cheong Hu, Lau","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hu et al., 2021] Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. Ogb-lsc: A large-scale challenge for machine learning on graphs. NeurIPS, 34, 2021.","title":"Ogb-lsc: A large-scale challenge for machine learning on graphs","authors":"Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Huang et al., 2021] Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou. Scaling up graph neural networks via graph coarsening. In KDD, 2021.","title":"Scaling up graph neural networks via graph coarsening","authors":"Huang","year":"2021","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jia et al., 2020] Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken. Redundancy-free computation for graph neural networks. In KDD, 2020.","title":"Redundancy-free computation for graph neural networks","authors":"Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Jin et al., 2022a] Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, and Bing Yin. Condensing graphs via one-step gradient matching. In KDD, 2022.","title":"Condensing graphs via one-step gradient matching","authors":"Jin","year":"2022","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Jin et al., 2022b] Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. Graph condensa- tion for graph neural networks. In ICLR, 2022.","title":"Graph condensation for graph neural networks","authors":"Jin","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Jin et al., 2023] Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah. Empowering graph rep- resentation learning with test-time graph transformation. In ICLR, 2023.","title":"Empowering graph representation learning with test-time graph transformation","authors":"Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Kemker and Kanan, 2018] Ronald Kemker and Christopher Kanan. Fearnet: Brain-inspired model for incremental learning. In ICLR, 2018.","title":"Fearnet: Brain-inspired model for incremental learning","authors":"Kanan Kemker, Ronald Kemker, Christopher Kanan","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[LeFevre and Terzi, 2010] Kristen LeFevre and Evimaria Terzi. Grass: Graph structure summarization. In SDM, 2010.","title":"Grass: Graph structure summarization","authors":"Terzi Lefevre, Kristen Lefevre, Evimaria Terzi","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2022] Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song. Graph condensation via receptive field distribution matching. arXiv, 2022.","title":"Graph condensation via receptive field distribution matching","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2023a] Yang Liu, Deyu Bo, and Chuan Shi. Graph condensation via eigenbasis matching. arXiv, 2023.","title":"Graph condensation via eigenbasis matching","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2023b] Yilun Liu, Ruihong Qiu, and Zi Huang. Cat: Balanced continual graph learning with graph con- densation. arXiv, 2023.","title":"Cat: Balanced continual graph learning with graph condensation","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2023c] Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, and Zi Huang. Puma: Efficient continual graph learning with graph condensation. arXiv, 2023.","title":"Puma: Efficient continual graph learning with graph condensation","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Loukas and Vandergheynst, 2018] Andreas Loukas and Pierre Vandergheynst. Spectrally approximating large graphs with smaller graphs. In ICML, 2018.","title":"Spectrally approximating large graphs with smaller graphs","authors":"Vandergheynst Loukas, Andreas Loukas, Pierre Vandergheynst","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mehrabi et al., 2021] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM computing surveys, 2021.","title":"A survey on bias and fairness in machine learning","authors":"Mehrabi","year":"2021","venue":"ACM computing surveys","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Nian et al., 2023] Yi Nian, Wei Jin, and Lu Lin. In-process global interpretation for graph learning via distribution matching. arXiv, 2023.","title":"In-process global interpretation for graph learning via distribution matching","authors":"Yi Nian, Wei Jin, Lu Lin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Pan et al., 2023] Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, and Weiqiang Wang. Fedgkd: Unleashing the power of collaboration in federated graph neural networks. arXiv, 2023.","title":"Fedgkd: Unleashing the power of collaboration in federated graph neural networks","authors":"Pan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Razin et al., 2023] Noam Razin, Tom Verbin, and Nadav Cohen. On the ability of graph neural networks to model interactions between vertices. In NeurIPS, 2023.","title":"On the ability of graph neural networks to model interactions between vertices","authors":"Razin","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Sachdeva and McAuley, 2023] Noveen Sachdeva and Julian McAuley. Data distillation: A survey. arXiv, 2023.","title":"Data distillation: A survey","authors":"Mcauley Sachdeva, Noveen Sachdeva, Julian Mcauley","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Sener and Savarese, 2018] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks:a core- set approach. In ICLR, 2018.","title":"Active learning for convolutional neural networks:a coreset approach","authors":"Ozan Sener, Silvio Savarese","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Shuman et al., 2015] David I Shuman, Mohammad Javad Faraji, and Pierre Vandergheynst. A multiscale pyramid transform for graph signals. IEEE Transactions on Signal Processing, 2015.","title":"A multiscale pyramid transform for graph signals","authors":"Mohammad Javad David I Shuman, Pierre Faraji, Vandergheynst","year":"2015","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Spielman and Srivastava, 2008] Daniel A Spielman and Nikhil Srivastava. Graph sparsification by effective resis- tances. In STOC, 2008.","title":"Graph sparsification by effective resistances","authors":"A Daniel, Nikhil Spielman, Srivastava","year":"2008","venue":"STOC","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Sugiyama and Sato, 2023] Tomohiro Sugiyama and Kazuhiro Sato. Kron reduction and effective resistance of directed graphs. SIAM Journal on Matrix Analysis and Applications, 2023.","title":"Kron reduction and effective resistance of directed graphs","authors":"Tomohiro Sugiyama, Kazuhiro Sato","year":"2023","venue":"SIAM Journal on Matrix Analysis and Applications","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Batson et al., 2013] Batson et al. Spectral sparsification of graphs: theory and algorithms. Communications of the ACM, 2013.","title":"Spectral sparsification of graphs: theory and algorithms","authors":"Batson","year":"2013","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Buffelli et al., 2022] Buffelli et al. Sizeshiftreg: a regular- ization method for improving size-generalization in graph neural networks. NeurIPS, 2022.","title":"Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks","authors":"Buffelli","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Ding et al., 2022] Ding et al. Faster hyperparameter search for gnns via calibrated dataset condensation. arXiv, 2022.","title":"Faster hyperparameter search for gnns via calibrated dataset condensation","authors":"Ding","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Dorfler et. al, 2012] Dorfler et. al. Kron reduction of graphs with applications to electrical networks. IEEE TCS-I, 2012.","title":"Kron reduction of graphs with applications to electrical networks","authors":"Dorfler","year":"2012","venue":"IEEE TCS-I","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Du et al., 2019] Du et al. Graph neural tangent kernel: Fus- ing graph neural networks with graph kernels. NeurIPS, 2019.","title":"Graph neural tangent kernel: Fusing graph neural networks with graph kernels","authors":"Du","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Gao et al., 2023] Gao et al. Graph condensation for induc- tive node representation learning. arXiv, 2023.","title":"Graph condensation for inductive node representation learning","authors":"Gao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Gao et al., 2024] Gao et al. Graph condensation: A survey. arXiv, 2024.","title":"Graph condensation: A survey","authors":"Gao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Geng et al., 2023] Geng et al. A survey on dataset dis- tillation: Approaches, applications and future directions. arXiv, 2023.","title":"A survey on dataset distillation: Approaches, applications and future directions","authors":"Geng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/741"},{"position":48,"raw":"[Imre et al., 2020] Imre et al. Spectrum-preserving sparsi- fication for visualization of big graphs. Computers & Graphics, 2020.","title":"Spectrum-preserving sparsification for visualization of big graphs","authors":"Imre","year":"2020","venue":"Computers & Graphics","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Kumar et al., 2023] Kumar et al. Featured graph coarsening with similarity guarantees. In ICML, 2023.","title":"Featured graph coarsening with similarity guarantees","authors":"Kumar","year":"2023","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Li et al., 2023] Li et al. Interpretable sparsification of brain graphs: Better practices and effective designs for graph neural networks. In KDD, 2023.","title":"Interpretable sparsification of brain graphs: Better practices and effective designs for graph neural networks","authors":"Li","year":"2023","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Liu et al., 2022] Liu et al. Survey on graph neural network acceleration: An algorithmic perspective. In IJCAI, 2022.","title":"Survey on graph neural network acceleration: An algorithmic perspective","authors":"Liu","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/772"},{"position":52,"raw":"[Mao et al., 2023] Mao et al. Gcare: Mitigating subgroup unfairness in graph condensation through adversarial reg- ularization. Applied Sciences, 2023.","title":"Gcare: Mitigating subgroup unfairness in graph condensation through adversarial regularization","authors":"Mao","year":"2023","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Purohit et al., 2014] Purohit et al. Fast influence-based coarsening for large networks. In KDD, 2014.","title":"Fast influence-based coarsening for large networks","authors":"Purohit","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Riondato et al., 2017] Riondato et al. Graph summarization with quality guarantees. Data mining and knowledge dis- covery, 2017.","title":"Riondato et al. Graph summarization with quality guarantees","authors":"Riondato","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Salha et al., 2019] Salha et al. A degeneracy framework for scalable graph autoencoders. In IJCAI, 2019.","title":"A degeneracy framework for scalable graph autoencoders","authors":"Salha","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/465"},{"position":56,"raw":"[Xu et al., 2023] Xu et al. Kernel ridge regression-based graph dataset distillation. In KDD, 2023.","title":"Kernel ridge regression-based graph dataset distillation","authors":"Xu","year":"2023","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Yang et al., 2023] Yang et al. Does graph distillation see like vision dataset counterpart? arXiv, 2023.","title":"Does graph distillation see like vision dataset counterpart?","authors":"Yang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Ying et al., 2019] Ying et al. Gnnexplainer: Generating ex- planations for graph neural networks. NeurIPS, 2019.","title":"Gnnexplainer: Generating explanations for graph neural networks","authors":"Ying","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Zhang et al., 2023] Zhang et al. A survey on graph neu- ral network acceleration: Algorithms, systems, and cus- tomized hardware. arXiv, 2023.","title":"A survey on graph neural network acceleration: Algorithms, systems, and customized hardware","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhang et al., 2024a] Zhang et al. Two heads are better than one: Boosting graph sparse training via semantic and topo- logical awareness. arXiv, 2024.","title":"Two heads are better than one: Boosting graph sparse training via semantic and topological awareness","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Zhang et al., 2024b] Zhang et al. Two trades is not baffled: Condense graph via crafting rational gradient matching. arXiv, 2024.","title":"Two trades is not baffled: Condense graph via crafting rational gradient matching","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Zheng et al., 2023] Zheng et al. Structure-free graph con- densation: From large-scale graphs to condensed graph- free data. NeurIPS, 2023.","title":"Structure-free graph condensation: From large-scale graphs to condensed graphfree data","authors":"Zheng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Tian et al., 2008] Yuanyuan Tian, Richard A Hankins, and Jignesh M Patel. Efficient aggregation for graph summa- rization. In SIGMOD, 2008.","title":"Efficient aggregation for graph summarization","authors":"Yuanyuan Tian, Richard A Hankins, Jignesh M Patel","year":"2008","venue":"SIGMOD","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Wang et al., 2018] Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. arXiv, 2018.","title":"Dataset distillation","authors":"Wang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Wickman et al., 2022] Ryan Wickman, Xiaofei Zhang, and Weizi Li. A generic graph sparsification framework using deep reinforcement learning. In ICDM, 2022.","title":"A generic graph sparsification framework using deep reinforcement learning","authors":"Ryan Wickman, Xiaofei Zhang, Weizi Li","year":"2022","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. TNNLS, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Xiao et al., 2024] Zhenbang Xiao, Shunyu Liu, Yu Wang, Tongya Zheng, and Mingli Song. Disentangled condensa- tion for large-scale graphs. arXiv, 2024.","title":"Disentangled condensation for large-scale graphs","authors":"Xiao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Xu et al., 2024] Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, and Bu Jiajun. A survey on graph condensation. arXiv, 2024.","title":"A survey on graph condensation","authors":"Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, Bu Jiajun","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Yuan et al., 2020] Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. Xgnn: Towards model-level explanations of graph neural networks. In KDD, 2020.","title":"Xgnn: Towards model-level explanations of graph neural networks","authors":"Jiliang Hao Yuan, Xia Tang, Shuiwang Hu, Ji","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Zhang et al., 2023] Xikun Zhang, Dongjin Song, and Dacheng Tao. Ricci curvature-based graph sparsification for continual graph representation learning. TNNLS, 2023.","title":"Ricci curvature-based graph sparsification for continual graph representation learning","authors":"Zhang","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Zhao et al., 2018] Zhiqiang Zhao, Yongyu Wang, and Zhuo Feng. Nearly-linear time spectral graph reduction for scal- able graph partitioning and data visualization. arXiv, 2018.","title":"Nearly-linear time spectral graph reduction for scalable graph partitioning and data visualization","authors":"Zhiqiang Zhao, Yongyu Wang, Zhuo Feng","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}