Fetching the paper…
Reading the bibliography…
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks.
Visualizing Data Using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Herding Dynamical Weights to Learn. In International Conference on Machine Learning (ICML) . 1121–1128
Max Welling. 2009 · 2009
Earlier work this paper cites.
iCaRL: Incremental Classifier and Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 2001–2010
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017 · 2010
Earlier work this paper cites.
Spectral Sparsification of Graphs: Theory and Algorithms
Joshua Batson, Daniel A Spielman, Nikhil Srivastava, and Shang-Hua Teng. 2013 · 2013
Earlier work this paper cites.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Earlier work this paper cites.
Variational graph auto-encoders. In In Neural Information Processing Systems Workshop . 1–3
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Mgae: Marginalized graph autoencoder for graph clustering. In Proceedings of ACM on Conference on Information and Knowledge Management (CIKM) . 889–898
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. 2017 · 2017
Earlier work this paper cites.
Semi-supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR)
Max Welling and Thomas N Kipf. 2017 · 2017
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. 2018 · 2018
Earlier work this paper cites.
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, and Clément Hongler. 2018 · 2018
Earlier work this paper cites.
Adversarially regularized graph autoencoder for graph embedding. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) . 2609–2615
Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. 2018 · 2018
Earlier work this paper cites.
Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations (ICLR)
Ozan Sener and Silvio Savarese. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In International Conference on Learning Representations (ICLR)
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018 · 2018
Earlier work this paper cites.
Cluster-GCN: An Efficient Algorithm For Training Deep And Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD) . 257–266
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019 · 2019
Earlier work this paper cites.
Pre-training of graph augmented transformers for medication recommendation. In International Joint Conference on Artificial Intelligence (IJCAI) . 5953–5959
Junyuan Shang, Tengfei Ma, Cao Xiao, and Jimeng Sun. 2019 · 2019
Earlier work this paper cites.
Attributed Graph Clustering: A Deep Attentional Embedding Approach. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)
C Wang, S Pan, R Hu, G Long, J Jiang, and C Zhang. 2019 · 2019
Earlier work this paper cites.
Simplifying Graph Convolutional Networks. In International Conference on Machine Learning (ICML) . PMLR, 6861–6871
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Earlier work this paper cites.
GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations (ICLR)
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019 · 2019
Earlier work this paper cites.
Attributed graph clustering via adaptive graph convolution. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) . 4327–4333
Xiaotong Zhang, Han Liu, Qimai Li, and Xiao Ming Wu. 2019 · 2019
Earlier work this paper cites.
Daniel Zügner and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
Graph Coarsening with Neural Networks. In International Conference on Learning Representations(ICLR)
Chen Cai, Dingkang Wang, and Yusu Wang. 2020 · 2020
Cited alongside, same era.
Graph Neural Architecture Search.. In International Joint Conferences on Artificial Intelligence (IJCAI) , Vol. 20. 1403–1409
Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020 · 2020
Cited alongside, same era.
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 (ICAIS) . PMLR, 4452–4462
Graph Self-supervised Learning: A Survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip Yu. 2022a · 2022
Later among the works it cites.
Fast Finite Width Neural Tangent Kernel. In International Conference on Machine Learning (ICML) . PMLR, 17018–17044
Roman Novak, Jascha Sohl-Dickstein, and Samuel S Schoenholz. 2022 · 2022
Later among the works it cites.
Distilled replay: Overcoming forgetting through synthetic samples. In Continual Semi-Supervised Learning: First International Workshop (CSSL) . Springer, 104–117
Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu. 2022 · 2022
Later among the works it cites.
Sample condensation in online continual learning. In International Joint Conference on Neural Networks (IJCNN) . IEEE, 01–08
Mattia Sangermano, Antonio Carta, Andrea Cossu, and Davide Bacciu. 2022 · 2022
Later among the works it cites.
Graph Sanitation with Application to Node Classification. In Proceedings of the ACM Web Conference (WWW) . 1136–1147
Zhe Xu, Boxin Du, and Hanghang Tong. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yu Jin, Andreas Loukas, and Joseph JaJa. 2020 · 2020
Cited alongside, same era.
Dataset Meta-Learning from Kernel Ridge-Regression. In International Conference on Learning Representations (ICLR)
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. 2020 · 2020
Cited alongside, same era.
Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In Proceedings of the Web Conference (WWW) . 673–683
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar. 2020 · 2020
Cited alongside, same era.
Dataset Condensation with Gradient Matching. In International Conference on Learning Representations (ICLR)
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2020
Cited alongside, same era.
A Unified Lottery Ticket Hypothesis For Graph Neural Networks. In International Conference on Machine Learning (ICML) . PMLR, 1695–1706
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021 · 2021
Cited alongside, same era.
Inheritance-guided hierarchical assignment for clinical automatic diagnosis. In International Conference on Database Systems for Advanced Applications (DASFAA) . Springer, 461–477
Yichao Du, Pengfei Luo, Xudong Hong, Tong Xu, Zhe Zhang, Chao Ren, Yi Zheng, and Enhong Chen. 2021 · 2021
Cited alongside, same era.
VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning. In Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD) . Springer, 103–115
Viresh Gupta and Tanmoy Chakraborty. 2021 · 2021
Cited alongside, same era.
Search to aggregate neighborhood for graph neural network. In International Conference on Data Engineering (ICDE) . IEEE, 552–563
ZHAO Huan, YAO Quanming, and TU Weiwei. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, and Jian Pei. 2022b · 2022
Later among the works it cites.
Projective ranking-based gnn evasion attacks
He Zhang, Xingliang Yuan, Chuan Zhou, and Shirui Pan. 2022d · 2022
Later among the works it cites.
Graph Neural Networks for Graphs with Heterophily: A Survey
Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. 2022a · 2022
Later among the works it cites.
Graph lifelong learning: A survey
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, and Charu Aggarwal. 2023 · 2023
Closest in time.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al · 2023
Closest in time.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, and Hui Xiong. 2023b · 2023
Closest in time.
A Comprehensive Survey to Dataset Distillation
Shiye Lei and Dacheng Tao. 2023 · 2023
Closest in time.
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2023b · 2023
Closest in time.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023a · 2023
Closest in time.
Integrating Graphs with Large Language Models: Methods and Prospects
Shirui Pan, Yizhen Zheng, and Yixin Liu. 2023b · 2023
Closest in time.
Noveen Sachdeva and Julian McAuley. 2023 · 2023
Closest in time.
Federated learning on non-iid graphs via structural knowledge sharing. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 9953–9961
Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. 2023 · 2023
Closest in time.
Continual Graph Learning: A Survey
Qiao Yuan, Sheng-Uei Guan, Pin Ni, Tianlun Luo, Ka Lok Man, Prudence Wong, and Victor Chang. 2023 · 2023
Closest in time.
On the interaction between node fairness and edge privacy in graph neural networks
He Zhang, Xingliang Yuan, Quoc Viet Hung Nguyen, and Shirui Pan. 2023b · 2023
Closest in time.
Dataset Condensation with Distribution Matching. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Bo Zhao and Hakan Bilen. 2023 · 2023
Closest in time.
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. 2023a · 2023
Closest in time.
Auto-heg: Automated graph neural network on heterophilic graphs
Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, and Shirui Pan. 2023b · 2023
Closest in time.