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. 2106.13427
  4. Cited By
Reliable Graph Neural Network Explanations Through Adversarial Training

Reliable Graph Neural Network Explanations Through Adversarial Training

25 June 2021
Donald Loveland
Shusen Liu
B. Kailkhura
A. Hiszpanski
Yong Han
    AAML
ArXiv (abs)PDFHTML

Papers citing "Reliable Graph Neural Network Explanations Through Adversarial Training"

15 / 15 papers shown
Title
GraphSVX: Shapley Value Explanations for Graph Neural Networks
GraphSVX: Shapley Value Explanations for Graph Neural Networks
Alexandre Duval
Fragkiskos D. Malliaros
FAtt
62
90
0
18 Apr 2021
Generative Causal Explanations for Graph Neural Networks
Generative Causal Explanations for Graph Neural Networks
Wanyu Lin
Hao Lan
Baochun Li
CML
59
177
0
14 Apr 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Ana Lucic
Maartje ter Hoeve
Gabriele Tolomei
Maarten de Rijke
Fabrizio Silvestri
156
145
0
05 Feb 2021
Graph Neural Network for Traffic Forecasting: A Survey
Graph Neural Network for Traffic Forecasting: A Survey
Weiwei Jiang
Jiayun Luo
GNNAI4TSAI4CE
222
877
0
27 Jan 2021
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph
  Neural Networks
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Minh Nhat Vu
My T. Thai
BDL
75
337
0
12 Oct 2020
Towards an Efficient and General Framework of Robust Training for Graph
  Neural Networks
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Kaidi Xu
Sijia Liu
Pin-Yu Chen
Mengshu Sun
Caiwen Ding
B. Kailkhura
Xinyu Lin
OODAAML
39
7
0
25 Feb 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural
  Networks
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Q. Huang
M. Yamada
Yuan Tian
Dinesh Singh
Dawei Yin
Yi-Ju Chang
FAtt
90
357
0
17 Jan 2020
Generative Counterfactual Introspection for Explainable Deep Learning
Generative Counterfactual Introspection for Explainable Deep Learning
Shusen Liu
B. Kailkhura
Donald Loveland
Yong Han
113
90
0
06 Jul 2019
Adversarial Examples Are Not Bugs, They Are Features
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
SILM
91
1,843
0
06 May 2019
GNNExplainer: Generating Explanations for Graph Neural Networks
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying
Dylan Bourgeois
Jiaxuan You
Marinka Zitnik
J. Leskovec
LLMAG
150
1,328
0
10 Mar 2019
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAttAAMLXAI
141
1,970
0
08 Oct 2018
Convolutional Neural Network Architectures for Signals Supported on
  Graphs
Convolutional Neural Network Architectures for Signals Supported on Graphs
Fernando Gama
A. Marques
G. Leus
Alejandro Ribeiro
182
288
0
01 May 2018
Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer
S. Schoenholz
Patrick F. Riley
Oriol Vinyals
George E. Dahl
596
7,485
0
04 Apr 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
644
29,076
0
09 Sep 2016
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
280
19,107
0
20 Dec 2014
1