GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation

12 September 2019

Papers citing "GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation"

13 / 13 papers shown

Title
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT Jiawei Zhang LRM 29 76 0 10 Apr 2023
Molecular Joint Representation Learning via Multi-modal Information Tianyu Wu Yang Tang Qiyu Sun Luolin Xiong 11 14 0 25 Nov 2022
NDGGNET-A Node Independent Gate based Graph Neural Networks Ye Tang Xuesong Yang Xinrui Liu Xiwei Zhao Zhangang Lin Changping Peng GNN 19 0 0 11 May 2022
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network Jiyang Bai Yuxiang Ren Jiawei Zhang 25 3 0 30 Dec 2021
Learning Attributed Graph Representations with Communicative Message Passing Transformer Jianwen Chen Shuangjia Zheng Ying Song Jiahua Rao Yuedong Yang 19 46 0 19 Jul 2021
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems William Herzberg D. Rowe A. Hauptmann S. Hamilton GNN MedIm AI4CE 9 33 0 28 Mar 2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks Yifei Wang Yisen Wang Jiansheng Yang Zhouchen Lin GNN 30 77 0 22 Feb 2021
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning Yizhu Jiao Yun Xiong Jiawei Zhang Yao Zhang Tianqi Zhang Yangyong Zhu SSL 19 167 0 22 Sep 2020
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network Jiyang Bai Yuxiang Ren Jiawei Zhang GNN 19 29 0 17 Feb 2020
Segmented Graph-Bert for Graph Instance Modeling Jiawei Zhang SSeg 20 6 0 09 Feb 2020
Graph Neural Ordinary Differential Equations Michael Poli Stefano Massaroli Junyoung Park Atsushi Yamashita Hajime Asama Jinkyoo Park AI4CE 28 154 0 18 Nov 2019
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization Peter L. Bartlett S. Evans Philip M. Long 66 31 0 13 Apr 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 1,811 0 25 Nov 2016