ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1905.07953
  4. Cited By
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph
  Convolutional Networks
v1v2 (latest)

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

20 May 2019
Wei-Lin Chiang
Xuanqing Liu
Si Si
Yang Li
Samy Bengio
Cho-Jui Hsieh
    GNN
ArXiv (abs)PDFHTMLGithub (35633★)

Papers citing "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks"

14 / 514 papers shown
Title
Ripple Walk Training: A Subgraph-based training framework for Large and
  Deep Graph Neural Network
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
Jiyang Bai
Yuxiang Ren
Jiawei Zhang
GNN
94
32
0
17 Feb 2020
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Anees Kazi
Luca Cosmo
Seyed-Ahmad Ahmadi
Nassir Navab
M. Bronstein
GNNMedIm
91
133
0
11 Feb 2020
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Guillaume Salha-Galvan
Romain Hennequin
Jean-Baptiste Remy
Manuel Moussallam
Michalis Vazirgiannis
GNNBDL
81
6
0
05 Feb 2020
Node Masking: Making Graph Neural Networks Generalize and Scale Better
Node Masking: Making Graph Neural Networks Generalize and Scale Better
Pushkar Mishra
Aleksandra Piktus
Gerard Goossen
Fabrizio Silvestri
AI4CE
68
14
0
17 Jan 2020
Layer-Dependent Importance Sampling for Training Deep and Large Graph
  Convolutional Networks
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou
Ziniu Hu
Yewen Wang
Song Jiang
Yizhou Sun
Quanquan Gu
GNN
107
285
0
17 Nov 2019
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video
  Prediction
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
T. Hascoet
AI4TS
49
3
0
24 Oct 2019
DeepGCNs: Making GCNs Go as Deep as CNNs
DeepGCNs: Making GCNs Go as Deep as CNNs
Ge Li
Matthias Muller
Guocheng Qian
Itzel C. Delgadillo
Abdulellah Abualshour
Ali K. Thabet
Guohao Li
3DPCGNN
98
176
0
15 Oct 2019
Rethinking Kernel Methods for Node Representation Learning on Graphs
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian
Long Zhao
Xi Peng
Dimitris N. Metaxas
64
24
0
06 Oct 2019
Multi-scale Attributed Node Embedding
Multi-scale Attributed Node Embedding
Benedek Rozemberczki
Carl Allen
Rik Sarkar
GNN
286
867
0
28 Sep 2019
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Kaixiong Zhou
Qingquan Song
Xiao Huang
Helen Zhou
GNN
128
186
0
07 Sep 2019
GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng
Hongkuan Zhou
Ajitesh Srivastava
Rajgopal Kannan
Viktor Prasanna
GNN
143
970
0
10 Jul 2019
Constant Time Graph Neural Networks
Constant Time Graph Neural Networks
Ryoma Sato
M. Yamada
H. Kashima
GNN
79
10
0
23 Jan 2019
A Comprehensive Survey on Graph Neural Networks
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu
Shirui Pan
Fengwen Chen
Guodong Long
Chengqi Zhang
Philip S. Yu
FaMLGNNAI4TSAI4CE
843
8,633
0
03 Jan 2019
Graph Neural Networks: A Review of Methods and Applications
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou
Ganqu Cui
Shengding Hu
Zhengyan Zhang
Cheng Yang
Zhiyuan Liu
Lifeng Wang
Changcheng Li
Maosong Sun
AI4CEGNN
1.2K
5,596
0
20 Dec 2018
Previous
123...10119