Beyond Real-world Benchmark Datasets: An Empirical Study of Node
Classification with GNNs

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs

18 June 2022

Makoto Onizuka

Papers citing "Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs"

9 / 9 papers shown

Title
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning Srinitish Srinivasan Omkumar CU SSL BDL 49 0 0 02 Feb 2025
Re-evaluating the Advancements of Heterophilic Graph Learning Sitao Luan Qincheng Lu Chenqing Hua Xinyu Wang Jiaqi Zhu Xiao-Wen Chang 57 2 0 09 Sep 2024
GraphWorld: Fake Graphs Bring Real Insights for GNNs John Palowitch Anton Tsitsulin Brandon Mayer Bryan Perozzi GNN 195 68 0 28 Feb 2022
Heterogeneous Graph Transformer Ziniu Hu Yuxiao Dong Kuansan Wang Yizhou Sun 185 1,170 0 03 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 189 916 0 02 Mar 2020
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 169 1,078 0 13 Feb 2020
Contextual Stochastic Block Models Y. Deshpande Andrea Montanari Elchanan Mossel S. Sen 103 152 0 23 Jul 2018
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 267 1,945 0 09 Jun 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 251 1,811 0 25 Nov 2016