Going Deeper into Permutation-Sensitive Graph Neural Networks

28 May 2022

Papers citing "Going Deeper into Permutation-Sensitive Graph Neural Networks"

6 / 6 papers shown

Title
Scalable Graph Compressed Convolutions Junshu Sun Chen Yang Shuhui Wang Qingming Huang GNN 45 0 0 26 Jul 2024
The Expressive Power of Graph Neural Networks: A Survey Bingxue Zhang Changjun Fan Shixuan Liu Kuihua Huang Xiang Zhao Jin-Yu Huang Zhong Liu 40 19 0 16 Aug 2023
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks Cai Zhou Xiyuan Wang Muhan Zhang 35 14 0 08 May 2023
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 38 18 0 21 Jul 2021
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
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li ... J. Zhao Haotong Zhang Alex Smola Jinyang Li Zheng-Wei Zhang AI4CE GNN 200 746 0 03 Sep 2019