Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

The PageRank citation ranking: Bringing order to the web

Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999

Earlier work this paper cites.

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016

Earlier work this paper cites.

How to learn a graph from smooth signals. In Artificial intelligence and statistics . PMLR, 920–929

Vassilis Kalofolias. 2016 · 2016

Earlier work this paper cites.

Neural message passing for quantum chemistry. In International Conference on Machine Learning . PMLR, 1263–1272

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017

Earlier work this paper cites.

Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA . 1024–1034

William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017

Earlier work this paper cites.

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

Thomas N. Kipf and Max Welling. 2017 · 2017

Earlier work this paper cites.

Spectrally Approximating Large Graphs with Smaller Graphs. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 (Proceedings of Machine Learning Research)

Andreas Loukas and Pierre Vandergheynst. 2018 · 2018

Earlier work this paper cites.

Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018

Earlier work this paper cites.

Dataset distillation

Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018 · 2018

Earlier work this paper cites.

A unifying framework for spectrum-preserving graph sparsification and coarsening

Gecia Bravo Hermsdorff and Lee Gunderson. 2019 · 2019

Earlier work this paper cites.

Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations (ICLR)

Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann. 2019 · 2019

Earlier work this paper cites.

Graph Reduction with Spectral and Cut Guarantees

Andreas Loukas. 2019 · 2019

Earlier work this paper cites.

Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization

Original

Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019 · 2019

Earlier work this paper cites.

Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA . 6861–6871

Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019 · 2019

Earlier work this paper cites.

Open graph benchmark: Datasets for machine learning on graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020

Earlier work this paper cites.

Temporal network representation learning via historical neighborhoods aggregation. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) . IEEE, 1117–1128

Shixun Huang, Zhifeng Bao, Guoliang Li, Yanghao Zhou, and J Shane Culpepper. 2020 · 2020

Earlier work this paper cites.

GraphSAINT: Graph Sampling Based Inductive Learning Method. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net

Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020 · 2020

Earlier work this paper cites.

Simple spectral graph convolution. In International Conference on Learning Representations

Hao Zhu and Piotr Koniusz. 2020 · 2020

Earlier work this paper cites.

Graph coarsening with neural networks

Original

Chen Cai, Dingkang Wang, and Yusu Wang. 2021 · 2021

Earlier work this paper cites.

A unified lottery ticket hypothesis for graph neural networks. In International conference on machine learning . PMLR, 1695–1706

Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021 · 2021

Earlier work this paper cites.

Scaling up graph neural networks via graph coarsening. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining . 675–684

Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou. 2021 · 2021

Earlier work this paper cites.

Dataset Meta-Learning from Kernel Ridge-Regression. In ICLR

Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. 2021 · 2021

Earlier work this paper cites.

Node Dependent Local Smoothing for Scalable Graph Learning

Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. 2021 · 2021

Earlier work this paper cites.

Dataset Condensation with Gradient Matching. In ICLR

Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021 · 2021

Cited alongside, same era.

Deep graph structure learning for robust representations: A survey

Original

Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang. 2021 · 2021

Cited alongside, same era.

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 · 2022

Cited alongside, same era.

Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation

Mucong Ding, Xiaoyu Liu, Tahseen Rabbani, Teresa Ranadive, Tai-Ching Tuan, and Furong Huang. 2022 · 2022

Cited alongside, same era.

Influence maximization in real-world closed social networks

Original

Shixun Huang, Wenqing Lin, Zhifeng Bao, and Jiachen Sun. 2022 · 2022

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data. In NeurIPS

Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan. 2023 · 2023

Later among the works it cites.

Graph coarsening via convolution matching for scalable graph neural network training. In Companion Proceedings of the ACM on Web Conference 2024 . 1502–1510

Charles Dickens, Edward Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, and Danai Koutra. 2024 · 2024

Closest in time.

The Faiss library

Original

Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024 · 2024

Closest in time.

Exgc: Bridging efficiency and explainability in graph condensation

Original

Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, and Xiangnan He. 2024 · 2024

Closest in time.