A Metadata-Driven Approach to Understand Graph Neural Networks

A Metadata-Driven Approach to Understand Graph Neural Networks

30 October 2023

Jiaqi Ma

Papers citing "A Metadata-Driven Approach to Understand Graph Neural Networks"

6 / 6 papers shown

Title
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark Ruizhong Qiu Weiliang Will Zeng Hanghang Tong James Ezick Christopher Lott 86 15 0 20 Feb 2025
Uplifting Message Passing Neural Network with Graph Original Information Xiao Liu Lijun Zhang Hui Guan GNN 13 1 0 08 Oct 2022
GraphWorld: Fake Graphs Bring Real Insights for GNNs John Palowitch Anton Tsitsulin Brandon Mayer Bryan Perozzi GNN 183 68 0 28 Feb 2022
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 167 1,058 0 13 Feb 2020
Contextual Stochastic Block Models Y. Deshpande Andrea Montanari Elchanan Mossel S. Sen 98 151 0 23 Jul 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 234 1,801 0 25 Nov 2016