Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning

The Laplacian spectrum of graphs

Bojan Mohar, Y Alavi, G Chartrand, and Ortrud Oellermann. 1991 · 1991

Earlier work this paper cites.

Spectral graph theory . Vol. 92

Fan RK Chung. 1997 · 1997

Earlier work this paper cites.

Graph data condensation via self-expressive graph structure reconstruction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1992–2002

Zhanyu Liu, Chaolv Zeng, and Guanjie Zheng. 2024c · 2002

Earlier work this paper cites.

A tutorial on spectral clustering

Ulrike Von Luxburg. 2007 · 2007

Earlier work this paper cites.

Facility location: concepts, models, algorithms and case studies

Reza Zanjirani Farahani and Masoud Hekmatfar. 2009 · 2009

Earlier work this paper cites.

Herding dynamical weights to learn. In Proceedings of the 26th Annual International Conference on Machine Learning . 1121–1128

Max Welling. 2009 · 2009

Earlier work this paper cites.

Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel. In 2013 IEEE global conference on signal and information processing . IEEE, 419–422

David K Hammond, Yaniv Gur, and Chris R Johnson. 2013 · 2013

Earlier work this paper cites.

Mysteries around the graph Laplacian eigenvalue 4

Yuji Nakatsukasa, Naoki Saito, and Ernest Woei. 2013 · 2013

Earlier work this paper cites.

Multiway spectral partitioning and higher-order cheeger inequalities

James R Lee, Shayan Oveis Gharan, and Luca Trevisan. 2014 · 2014

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.

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.

Active learning for convolutional neural networks: A core-set approach

Original

Ozan Sener and Silvio Savarese. 2017 · 2017

Earlier work this paper cites.

Deep graph infomax

Original

Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018 · 2018

Earlier work this paper cites.

Graph Reduction with Spectral and Cut Guarantees

Andreas Loukas. 2019 · 2019

Earlier work this paper cites.

Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning . PMLR, 6861–6871

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

Earlier work this paper cites.

Scaling Graph Neural Networks with Approximate PageRank. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, New York, NY, USA

Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020 · 2020

Earlier work this paper cites.

Try this instead: Personalized and interpretable substitute recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 891–900

Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, and Meng Wang. 2020 · 2020

Earlier work this paper cites.

Graph prototypical networks for few-shot learning on attributed networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 295–304

Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu. 2020 · 2020

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

Cited alongside, same era.

Graph coarsening with preserved spectral properties. In International Conference on Artificial Intelligence and Statistics . PMLR, 4452–4462

Yu Jin, Andreas Loukas, and Joseph JaJa. 2020 · 2020

Cited alongside, same era.

Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1150–1160

Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020

Cited alongside, same era.

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

Cited alongside, same era.

Comprehensive privacy analysis on federated recommender system against attribute inference attacks

Shijie Zhang, Wei Yuan, and Hongzhi Yin. 2023 · 2023

Later among the works it cites.

Dataset condensation with distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 6514–6523

Bo Zhao and Hakan Bilen. 2023 · 2023

Later among the works it cites.

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.

Condensed Sample-Guided Model Inversion for Knowledge Distillation

Original

Kuluhan Binici, Shivam Aggarwal, Cihan Acar, Nam Trung Pham, Karianto Leman, Gim Hee Lee, and Tulika Mitra. 2024 · 2024

Closest in time.

Graph contrastive learning with stable and scalable spectral encoding

Deyu Bo, Yuan Fang, Yang Liu, and Chuan Shi. 2024 · 2024