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Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains.
The transitive reduction of a directed graph
A. V. Aho, M. R. Garey, and J. D. Ullman · 1972
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On sparse spanners of weighted graphs
Ingo Althöfer, Gautam Das, David Dobkin, Deborah Joseph, and José Soares · 1993
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Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices
George Karypis and Vipin Kumar · 1997
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A simple linear time algorithm for computing a (2 k—1)-spanner of o (n 1+ 1/k) size in weighted graphs
Surender Baswana and Sandeep Sen · 2003
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Deeper inside pagerank
Amy N Langville and Carl D Meyer · 2004
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Large scale networks fingerprinting and visualization using the k-core decomposition
J Alvarez-Hamelin, Luca Dall’Asta, Alain Barrat, and Alessandro Vespignani · 2005
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Laplacians and the cheeger inequality for directed graphs
Fan Chung · 2005
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Coarse-graining and self-dissimilarity of complex networks
Shalev Itzkovitz, Reuven Levitt, Nadav Kashtan, Ron Milo, Michael Itzkovitz, and Uri Alon · 2005
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Laplacian energy of a graph
Ivan Gutman and Bo Zhou · 2006
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Weighted graph cuts without eigenvectors a multilevel approach
Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis · 2007
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Graph summarization with bounded error
Saket Navlakha, Rajeev Rastogi, and Nisheeth Shrivastava · 2008
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Graph sparsification by effective resistances
Daniel A Spielman and Nikhil Srivastava · 2008
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Efficient aggregation for graph summarization
Yuanyuan Tian, Richard A Hankins, and Jignesh M Patel · 2008
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Twice-ramanujan sparsifiers
Joshua D Batson, Daniel A Spielman, and Nikhil Srivastava · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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Multilevel manifold learning with application to spectral clustering
Haw-ren Fang, Sophia Sakellaridi, and Yousef Saad · 2010
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Grass: Graph structure summarization
Kristen LeFevre and Evimaria Terzi · 2010
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Discovery-driven graph summarization
Ning Zhang, Yuanyuan Tian, and Jignesh M Patel · 2010
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Algebraic distance on graphs
Jie Chen and Ilya Safro · 2011
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Kron reduction of graphs with applications to electrical networks
Florian Dorfler and Francesco Bullo · 2012
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Lean algebraic multigrid (lamg): Fast graph laplacian linear solver
Oren E Livne and Achi Brandt · 2012
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Spectral sparsification of graphs: theory and algorithms
Joshua Batson, Daniel A Spielman, Nikhil Srivastava, and Shang-Hua Teng · 2013
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An efficient algorithm for unweighted spectral graph sparsification
David G Anderson, Ming Gu, and Christopher Melgaard · 2014
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Identity obfuscation in graphs through the information theoretic lens
Francesco Bonchi, Aristides Gionis, and Tamir Tassa · 2014
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Fast influence-based coarsening for large networks
Manish Purohit, B Aditya Prakash, Chanhyun Kang, Yao Zhang, and VS Subrahmanian · 2014
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Graph summarization for attributed graphs
Ye Wu, Zhinong Zhong, Wei Xiong, and Ning Jing · 2014
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Set-based approximate approach for lossless graph summarization
Kifayat Ullah Khan, Waqas Nawaz, and Young-Koo Lee · 2015
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Advanced coarsening schemes for graph partitioning
Ilya Safro, Peter Sanders, and Christian Schulz · 2015
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A multiscale pyramid transform for graph signals
David I Shuman, Mohammad Javad Faraji, and Pierre Vandergheynst · 2015
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Distribution of laplacian eigenvalues of graphs
Kinkar Ch Das, Seyed Ahmad Mojallal, and Vilmar Trevisan · 2016
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Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturbation analysis
Zhuo Feng · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Graph summarization with quality guarantees
Matteo Riondato, David García-Soriano, and Francesco Bonchi · 2017
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Proje: Embedding projection for knowledge graph completion
Baoxu Shi and Tim Weninger · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Robust spatial filtering with graph convolutional neural networks
Felipe Petroski Such, Shagan Sah, Miguel Alexander Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan D Cahill, and Raymond Ptucha · 2017
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Netgist: Learning to generate task-based network summaries
Sorour E Amiri, Bijaya Adhikari, Aditya Bharadwaj, and B Aditya Prakash · 2018
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Scalable approximation algorithm for graph summarization
Maham Anwar Beg, Muhammad Ahmad, Arif Zaman, and Imdadullah Khan · 2018
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Harp: Hierarchical representation learning for networks
Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena · 2018
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Constructing linear-sized spectral sparsification in almost-linear time
Yin Tat Lee and He Sun · 2018
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Graph summarization methods and applications: A survey
Yike Liu, Tara Safavi, Abhilash Dighe, and Danai Koutra · 2018
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Spectrally approximating large graphs with smaller graphs
Andreas Loukas and Pierre Vandergheynst · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Leveraging multiple gene networks to prioritize gwas candidate genes via network representation learning
Mengmeng Wu, Wanwen Zeng, Wenqiang Liu, Hairong Lv, Ting Chen, and Rui Jiang · 2018
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Nearly-linear time spectral graph reduction for scalable graph partitioning and data visualization
Zhiqiang Zhao, Yongyu Wang, and Zhuo Feng · 2018
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On differentially private graph sparsification and applications
Raman Arora and Jalaj Upadhyay · 2019
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A unifying framework for spectrum-preserving graph sparsification and coarsening
Gecia Bravo Hermsdorff and Lee Gunderson · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Solving graph compression via optimal transport
Vikas Garg and Tommi Jaakkola · 2019
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Graph reduction with spectral and cut guarantees
Andreas Loukas · 2019
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Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
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A generic graph sparsification framework using deep reinforcement learning
Ryan Wickman, Xiaofei Zhang, and Weizi Li · 2022
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Handling distribution shifts on graphs: An invariance perspective
Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf · 2022
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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Graph data augmentation for graph machine learning: A survey
Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günneman, Neil Shah, and Meng Jiang · 2022
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Graph neural networks for graphs with heterophily: A survey
Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu · 2022
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
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A degeneracy framework for scalable graph autoencoders
Guillaume Salha, Romain Hennequin, Viet Anh Tran, and Michalis Vazirgiannis · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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About graph degeneracy, representation learning and scalability
Simon Brandeis, Adrian Jarret, and Pierre Sevestre · 2020
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Spectrum-preserving sparsification for visualization of big graphs
Martin Imre, Jun Tao, Yongyu Wang, Zhiqiang Zhao, Zhuo Feng, and Chaoli Wang · 2020
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Multilayer network simplification: approaches, models and methods
Roberto Interdonato, Matteo Magnani, Diego Perna, Andrea Tagarelli, and Davide Vega · 2020
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Data augmentation on graphs: A survey
Jiajun Zhou, Chenxuan Xie, Zhenyu Wen, Xiangyu Zhao, and Qi Xuan · 2022
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A survey on hypergraph representation learning
Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, and Dingqi Yang · 2023
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Generalizing downsampling from regular data to graphs
Davide Bacciu, Alessio Conte, and Francesco Landolfi · 2023
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Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Rui Bing, Guan Yuan, Mu Zhu, Fanrong Meng, Huifang Ma, and Shaojie Qiao · 2023
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Graph coarsening via convolution matching for scalable graph neural network training
Charles Dickens, Eddie Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, and Danai Koutra · 2023
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Fair graph distillation
Qizhang Feng, Zhimeng Jiang, Ruiquan Li, Yicheng Wang, Na Zou, Jiang Bian, and Xia Hu · 2023
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Multiple sparse graphs condensation
Jian Gao and Jianshe Wu · 2023
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Graph condensation for inductive node representation learning
Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, and Hongzhi Yin · 2023
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A survey on dataset distillation: Approaches, applications and future directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong · 2023
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Neighborhood homophily-based graph convolutional network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie, and Qi Xuan · 2023
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Mirage: Model-agnostic graph distillation for graph classification
Mridul Gupta, Sahil Manchanda, Sayan Ranu, and Hariprasad Kodamana · 2023
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Featured graph coarsening with similarity guarantees
Manoj Kumar, Anurag Sharma, Shashwat Saxena, and Sandeep Kumar · 2023
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Interpretable sparsification of brain graphs: Better practices and effective designs for graph neural networks
Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, and Yujun Yan · 2023
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Attend who is weak: Enhancing graph condensation via cross-free adversarial training
Xinglin Li, Kun Wang, Hanhui Deng, Yuxuan Liang, and Di Wu · 2023
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Comprehensive graph gradual pruning for sparse training in graph neural networks
Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, and Danilo P Mandic · 2023
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Graph condensation via eigenbasis matching
Yang Liu, Deyu Bo, and Chuan Shi · 2023
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Cat: Balanced continual graph learning with graph condensation
Yilun Liu, Ruihong Qiu, and Zi Huang · 2023
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Puma: Efficient continual graph learning with graph condensation
Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, and Zi Huang · 2023
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Gcare: Mitigating subgroup unfairness in graph condensation through adversarial regularization
Runze Mao, Wenqi Fan, and Qing Li · 2023
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In-process global interpretation for graph learning via distribution matching
Yi Nian, Wei Jin, and Lu Lin · 2023
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Fedgkd: Unleashing the power of collaboration in federated graph neural networks
Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, and Weiqiang Wang · 2023
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On the ability of graph neural networks to model interactions between vertices
Noam Razin, Tom Verbin, and Nadav Cohen · 2023
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Noveen Sachdeva and Julian McAuley · 2023
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A survey on graph neural networks for graph summarization
Nasrin Shabani, Jia Wu, Amin Beheshti, Jin Foo, Ambreen Hanif, and Maryam Shahabikargar · 2023
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Serving graph compression for graph neural networks
Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, and Sanjiv Kumar · 2023
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xgcn: An extreme graph convolutional network for large-scale social link prediction
Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, and Hai Jin · 2023
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Kron reduction and effective resistance of directed graphs
Tomohiro Sugiyama and Kazuhiro Sato · 2023
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Graph clustering with graph neural networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel Müller · 2023
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Kernel ridge regression-based graph dataset distillation
Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, and Hanghang Tong · 2023
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Does graph distillation see like vision dataset counterpart?
Beining Yang, Kai Wang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Hao Tang, Yang You, and Jianxin Li · 2023
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Data-centric ai: Perspectives and challenges
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, and Xia Hu · 2023
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A survey on graph neural network acceleration: Algorithms, systems, and customized hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Jason Cong, Yizhou Sun, et al · 2023
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Ricci curvature-based graph sparsification for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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Towards data-centric graph machine learning: Review and outlook
Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, and Shirui Pan · 2023
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Structure-free graph condensation: From large-scale graphs to condensed graph-free data
Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan · 2023
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Ising on the graph: Task-specific graph subsampling via the ising model
Maria Bånkestad, Jennifer Andersson, Sebastian Mair, and Jens Sjölund · 2024
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Graph-skeleton:˜ 1% nodes are sufficient to represent billion-scale graph
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Disentangled condensation for large-scale graphs
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