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Towards more practical adversarial attacks on graph neural networks
Jiaqi Ma
, Shuangrui Ding
, Qiaozhu Mei
Research output
:
Contribution to journal
›
Conference article
›
peer-review
Overview
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Dive into the research topics of 'Towards more practical adversarial attacks on graph neural networks'. Together they form a unique fingerprint.
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Keyphrases
Adversarial Attack
100%
Attack Strategy
20%
Attacker
20%
Backward Propagation
20%
Black Hole Attack
20%
Black-box Settings
20%
Classification Accuracy
60%
Classification Loss
20%
Common Graphs
20%
Diminishing Returns
40%
Effective Source
20%
Gradient-based
20%
Graph Neural Network
100%
Inductive Bias
20%
Large Margin
20%
Loss Classification
20%
Neural Network Model
20%
Node Selection
20%
PageRank
20%
Random Walk
20%
Real-time Constraints
20%
Return Patterns
40%
White-box Attack
20%
Computer Science
Adversarial Machine Learning
100%
Attackers
20%
Black-Box Attack
20%
Experimental Result
20%
Graph Neural Network
100%
Misclassification Rate
60%
Neural Network Model
20%
Random Walk
20%