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Perturb More, Trap More: Understanding Behaviors of Graph Neural
  Networks

Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks

21 April 2020
Chaojie Ji
Ruxin Wang
Hongyan Wu
ArXivPDFHTML

Papers citing "Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks"

4 / 4 papers shown
Title
Towards explainable message passing networks for predicting carbon
  dioxide adsorption in metal-organic frameworks
Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks
Ali Raza
Faaiq G. Waqar
Arni Sturluson
Cory M. Simon
Xiaoli Z. Fern
AI4CE
10
3
0
02 Dec 2020
Graph Polish: A Novel Graph Generation Paradigm for Molecular
  Optimization
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
Chaojie Ji
Yijia Zheng
Ruxin Wang
Yunpeng Cai
Hongyan Wu
26
18
0
14 Aug 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake
Oliver Eberle
Jonas Lederer
Shinichi Nakajima
Kristof T. Schütt
Klaus-Robert Muller
G. Montavon
34
215
0
05 Jun 2020
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
446
15,645
0
02 Nov 2015
1