Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

27 October 2020

Ser-Nam Lim

Papers citing "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks"

22 / 172 papers shown

Title
Graph Belief Propagation Networks Junteng Jia Cenk Baykal Vamsi K. Potluru Austin R. Benson GNN 19 3 0 06 Jun 2021
Independent Prototype Propagation for Zero-Shot Compositionality Frank Ruis Gertjan J. Burghouts Doina Bucur 16 53 0 01 Jun 2021
$$\ell_2$-norm Flow Diffusion in Near-Linear Time$ $\ell_2$ -norm Flow Diffusion in Near-Linear Time Li Chen Richard Peng Di Wang 19 0 0 30 May 2021
How Attentive are Graph Attention Networks? Shaked Brody Uri Alon Eran Yahav GNN 60 1,019 0 30 May 2021
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks Huixuan Chi Yuying Wang Qinfen Hao Hong Xia GNN 24 11 0 18 May 2021
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning Indro Spinelli Simone Scardapane Amir Hussain A. Uncini FaML 41 80 0 29 Apr 2021
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification Jie Chen Shouzhen Chen Mingyuan Bai Jian Pu Junping Zhang Junbin Gao 39 21 0 28 Apr 2021
SAS: A Simple, Accurate and Scalable Node Classification Algorithm Ziyuan Wang Fengzhao Yang Rui Fan GNN 30 0 0 19 Apr 2021
New Benchmarks for Learning on Non-Homophilous Graphs Derek Lim Xiuyu Li Felix Hohne Ser-Nam Lim 36 100 0 03 Apr 2021
A nonlinear diffusion method for semi-supervised learning on hypergraphs Francesco Tudisco Konstantin Prokopchik Austin R. Benson 22 12 0 27 Mar 2021
Bag of Tricks for Node Classification with Graph Neural Networks Yangkun Wang Jiarui Jin Weinan Zhang Yong Yu Zheng-Wei Zhang David Wipf 36 55 0 24 Mar 2021
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs Weihua Hu Matthias Fey Hongyu Ren Maho Nakata Yuxiao Dong J. Leskovec AI4CE 23 401 0 17 Mar 2021
Should Graph Neural Networks Use Features, Edges, Or Both? Lukas Faber Yifan Lu Roger Wattenhofer GNN 24 10 0 11 Mar 2021
CogDL: A Comprehensive Library for Graph Deep Learning Yukuo Cen Zhenyu Hou Yan Wang Qibin Chen Yi Luo ... Guohao Dai Yu Wang Chang Zhou Hongxia Yang Jie Tang GNN AI4CE 19 16 0 01 Mar 2021
Graph-based Semi-supervised Learning: A Comprehensive Review Zixing Song Xiangli Yang Zenglin Xu Irwin King 84 193 0 26 Feb 2021
How Framelets Enhance Graph Neural Networks Xuebin Zheng Bingxin Zhou Junbin Gao Yu Guang Wang Pietro Lio Ming Li Guido Montúfar 59 69 0 13 Feb 2021
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations Junteng Jia Austin R. Benson 29 28 0 19 Jan 2021
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification L. Jiang John Shi Mark Cheung Oren Wright J. M. F. Moura 10 0 0 16 Dec 2020
Revisiting graph neural networks and distance encoding from a practical view Haoteng Yin Yanbang Wang Pan Li AI4CE 34 5 0 22 Nov 2020
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation Hande Dong Jiawei Chen Fuli Feng Xiangnan He Shuxian Bi Zhaolin Ding Peng Cui BDL 33 102 0 23 Oct 2020
Message Passing Neural Processes Ben Day Cătălina Cangea Arian R. Jamasb Pietro Lio 27 11 0 29 Sep 2020
Understanding and Resolving Performance Degradation in Graph Convolutional Networks Kuangqi Zhou Yanfei Dong Kaixin Wang W. Lee Bryan Hooi Huan Xu Jiashi Feng GNN BDL 42 89 0 12 Jun 2020