Provably Powerful Graph Neural Networks for Directed Multigraphs

Provably Powerful Graph Neural Networks for Directed Multigraphs

20 June 2023

Luc von Niederhäusern

Roger Wattenhofer

Papers citing "Provably Powerful Graph Neural Networks for Directed Multigraphs"

7 / 7 papers shown

Title
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation Bruno Deprez Toon Vanderschueren Bart Baesens Tim Verdonck Wouter Verbeke 33 4 0 29 May 2024
Boosting the Cycle Counting Power of Graph Neural Networks with I $^2$ -GNNs Yinan Huang Xingang Peng Jianzhu Ma Muhan Zhang 76 46 0 22 Oct 2022
Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch B. Chamberlain Michael W. Mahoney Michael M. Bronstein Siddhartha Mishra 74 53 0 02 Oct 2022
A Theoretical Comparison of Graph Neural Network Extensions Pál András Papp Roger Wattenhofer 95 45 0 30 Jan 2022
Graph Neural Networks with Learnable Structural and Positional Representations Vijay Prakash Dwivedi A. Luu T. Laurent Yoshua Bengio Xavier Bresson GNN 192 306 0 15 Oct 2021
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 169 1,072 0 13 Feb 2020
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 258 1,398 0 01 Dec 2016