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Open Graph Benchmark: Datasets for Machine Learning on Graphs

2 May 2020

Yuxiao Dong

Papers citing "Open Graph Benchmark: Datasets for Machine Learning on Graphs"

50 / 1,644 papers shown

Title
$p$ -Laplacian Based Graph Neural Networks Guoji Fu P. Zhao Yatao Bian 59 45 0 14 Nov 2021
Implicit vs Unfolded Graph Neural Networks Yongyi Yang Tang Liu Yangkun Wang Zengfeng Huang David Wipf 236 15 0 12 Nov 2021
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs Hesham Mostafa GNN 99 25 0 11 Nov 2021
Implicit SVD for Graph Representation Learning Sami Abu-El-Haija Hesham Mostafa Marcel Nassar V. Crespi Greg Ver Steeg Aram Galstyan 64 5 0 11 Nov 2021
Graph Neural Network Training with Data Tiering S. Min Kun Wu Mert Hidayetoğlu Jinjun Xiong Xiang Song Wen-mei W. Hwu GNN 58 16 0 10 Nov 2021
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation Joonhyung Park Hajin Shim Eunho Yang 130 51 0 10 Nov 2021
On Representation Knowledge Distillation for Graph Neural Networks Chaitanya K. Joshi Fayao Liu Xu Xun Jie Lin Chuan-Sheng Foo 100 64 0 09 Nov 2021
MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers A. Young Bo Wang Hannes L. Röst 53 5 0 08 Nov 2021
Directional Message Passing on Molecular Graphs via Synthetic Coordinates Johannes Klicpera Chandan Yeshwanth Stephan Günnemann 72 38 0 08 Nov 2021
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning Qinkai Zheng Xu Zou Yuxiao Dong Yukuo Cen Da Yin Jiarong Xu Yang Yang Jie Tang OOD AAML 84 51 0 08 Nov 2021
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing Zhe Zhou Cong Li Xuechao Wei Xiaoyang Wang Guangyu Sun GNN 26 28 0 01 Nov 2021
Optimizing Sparse Matrix Multiplications for Graph Neural Networks Shenghao Qiu You Liang Zheng Wang GNN 53 18 0 30 Oct 2021
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction Eli Chien Wei-Cheng Chang Cho-Jui Hsieh Hsiang-Fu Yu Jiong Zhang O. Milenkovic Inderjit S Dhillon 237 140 0 29 Oct 2021
On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong M. Ramezani M. Mahdavi 72 76 0 28 Oct 2021
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs Hongyu Ren H. Dai Bo Dai Xinyun Chen Denny Zhou J. Leskovec Dale Schuurmans LRM 73 42 0 28 Oct 2021
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks Hao Xue Kaixiong Zhou Tianlong Chen Kai Guo Helen Zhou Yi Chang Xin Wang AAML 76 15 0 28 Oct 2021
RIM: Reliable Influence-based Active Learning on Graphs Wentao Zhang Yexin Wang Zhenbang You Mengyao Cao Ping Huang Jiulong Shan Zhi-Xin Yang Tengjiao Wang 86 32 0 28 Oct 2021
Towards a Taxonomy of Graph Learning Datasets Renming Liu Semih Cantürk Frederik Wenkel Dylan Sandfelder Devin Kreuzer ... Michal Perlmutter Bastian Rieck M. Hirn Guy Wolf Ladislav Rampášek 29 0 0 27 Oct 2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Derek Lim Felix Hohne Xiuyu Li Sijia Huang Vaishnavi Gupta Omkar Bhalerao Ser-Nam Lim 149 361 0 27 Oct 2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization Mucong Ding Kezhi Kong Jingling Li Chen Zhu John P. Dickerson Furong Huang Tom Goldstein GNN MQ 107 49 0 27 Oct 2021
Robustness of Graph Neural Networks at Scale Simon Geisler Tobias Schmidt Hakan cSirin Daniel Zügner Aleksandar Bojchevski Stephan Günnemann AAML 109 135 0 26 Oct 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler Bertrand Charpentier Simon Geisler Daniel Zügner Stephan Günnemann UQCV BDL 120 89 0 26 Oct 2021
Heterogeneous Temporal Graph Neural Network Yujie Fan Mingxuan Ju Chuxu Zhang Liang Zhao Yanfang Ye 67 38 0 26 Oct 2021
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features Jiuhai Chen Jonas W. Mueller V. Ioannidis Soji Adeshina Yangkun Wang Tom Goldstein David Wipf 102 12 0 26 Oct 2021
Nested Graph Neural Networks Muhan Zhang Pan Li 88 169 0 25 Oct 2021
SEA: Graph Shell Attention in Graph Neural Networks Christian Frey Yunpu Ma Matthias Schubert 86 1 0 20 Oct 2021
Steganography of Complex Networks Daewon Lee 33 1 0 20 Oct 2021
What is Learned in Knowledge Graph Embeddings? Michael R Douglas Michael Simkin Omri Ben-Eliezer Tianqi Wu Peter Chin Trung D. Q. Dang Andrew Wood 95 1 0 19 Oct 2021
Beltrami Flow and Neural Diffusion on Graphs B. Chamberlain J. Rowbottom D. Eynard Francesco Di Giovanni Xiaowen Dong M. Bronstein AI4CE 88 86 0 18 Oct 2021
ifMixup: Interpolating Graph Pair to Regularize Graph Classification Hongyu Guo Yongyi Mao 65 11 0 18 Oct 2021
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation Shichang Zhang Yozen Liu Yizhou Sun Neil Shah 97 185 0 17 Oct 2021
MG-GCN: Scalable Multi-GPU GCN Training Framework M. F. Balin Kaan Sancak Ümit V. Çatalyürek GNN 67 7 0 17 Oct 2021
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining Tim Kaler Nickolas Stathas Anne Ouyang A. Iliopoulos Tao B. Schardl C. E. Leiserson Jie Chen GNN 142 55 0 16 Oct 2021
Graph Neural Networks with Learnable Structural and Positional Representations Vijay Prakash Dwivedi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson GNN 297 331 0 15 Oct 2021
Graph Condensation for Graph Neural Networks Wei Jin Lingxiao Zhao Shichang Zhang Yozen Liu Jiliang Tang Neil Shah DD AI4CE 148 162 0 14 Oct 2021
Why Propagate Alone? Parallel Use of Labels and Features on Graphs Yangkun Wang Jiarui Jin Weinan Zhang Yongyi Yang Jiuhai Chen Quan Gan Yong Yu Zheng Zhang Zengfeng Huang David Wipf AAML 129 12 0 14 Oct 2021
SoGCN: Second-Order Graph Convolutional Networks Peihao Wang Yuehao Wang Hua Lin Jianbo Shi 87 3 0 14 Oct 2021
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation Cole Hawkins V. Ioannidis Soji Adeshina George Karypis GNN SSL 57 2 0 12 Oct 2021
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding L. Cappelletti Tommaso Fontana E. Casiraghi V. Ravanmehr Tiffany J. Callahan ... marcin p. joachimiak Christopher J. Mungall Peter N. Robinson Justin P Reese Giorgio Valentini 53 25 0 12 Oct 2021
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform Zhen Xu Sergio Escalera Isabelle M Guyon Adrien Pavao M. Richard Wei-Wei Tu Quanming Yao Huan Zhao 159 60 0 12 Oct 2021
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks Moshe Eliasof Ben Bodner Eran Treister GNN 79 9 0 10 Oct 2021
Machine Learning Featurizations for AI Hacking of Political Systems Nathan Sanders B. Schneier 60 2 0 08 Oct 2021
3D Infomax improves GNNs for Molecular Property Prediction Hannes Stärk Dominique Beaini Gabriele Corso Prudencio Tossou Christian Dallago Stephan Günnemann Pietro Lio AI4CE 119 208 0 08 Oct 2021
Stable Prediction on Graphs with Agnostic Distribution Shift Shengyu Zhang Kun Kuang J. Qiu Jin Yu Zhou Zhao Hongxia Yang Zhongfei Zhang Leilei Gan OOD 120 8 0 08 Oct 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Lingxiao Zhao Wei Jin Leman Akoglu Neil Shah GNN 144 165 0 07 Oct 2021
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation Dennis Ulmer Christian Hardmeier J. Frellsen BDL UQCV UD EDL PER 150 55 0 06 Oct 2021
Distributed Optimization of Graph Convolutional Network using Subgraph Variance Taige Zhao Xiangyu Song Jianxin Li Wei Luo Imran Razzak GNN 60 9 0 06 Oct 2021
Equivariant Subgraph Aggregation Networks Beatrice Bevilacqua Fabrizio Frasca Derek Lim Balasubramaniam Srinivasan Chen Cai G. Balamurugan M. Bronstein Haggai Maron 133 180 0 06 Oct 2021
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems A. Budhiraja 16 2 0 05 Oct 2021
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning Jun Xia Lirong Wu Ge Wang Jintao Chen Stan Z. Li 69 123 0 05 Oct 2021