2 research outputs found
A Survey of Adversarial Learning on Graphs
Deep learning models on graphs have achieved remarkable performance in
various graph analysis tasks, e.g., node classification, link prediction and
graph clustering. However, they expose uncertainty and unreliability against
the well-designed inputs, i.e., adversarial examples. Accordingly, a line of
studies have emerged for both attack and defense addressed in different graph
analysis tasks, leading to the arms race in graph adversarial learning.
Despite the booming works, there still lacks a unified problem definition and
a comprehensive review. To bridge this gap, we investigate and summarize the
existing works on graph adversarial learning tasks systemically. Specifically,
we survey and unify the existing works w.r.t. attack and defense in graph
analysis tasks, and give appropriate definitions and taxonomies at the same
time. Besides, we emphasize the importance of related evaluation metrics,
investigate and summarize them comprehensively. Hopefully, our works can
provide a comprehensive overview and offer insights for the relevant
researchers. More details of our works are available at
https://github.com/gitgiter/Graph-Adversarial-Learning.Comment: TKDD under revie
Adversarial Attack on Large Scale Graph
Recent studies have shown that graph neural networks are vulnerable against
perturbations due to lack of robustness and can therefore be easily fooled.
Most works on attacking the graph neural networks are currently mainly using
the gradient information to guide the attack and achieve outstanding
performance. Nevertheless, the high complexity of time and space makes them
unmanageable for large scale graphs. We argue that the main reason is that they
have to use the entire graph for attacks, resulting in the increasing time and
space complexity as the data scale grows. In this work, we propose an efficient
Simplified Gradient-based Attack (SGA) framework to bridge this gap. SGA can
cause the graph neural networks to misclassify specific target nodes through a
multi-stage optimized attack framework, which needs only a much smaller
subgraph. In addition, we present a practical metric named Degree Assortativity
Change (DAC) for measuring the impacts of adversarial attacks on graph data. We
evaluate our attack method on four real-world datasets by attacking several
commonly used graph neural networks. The experimental results show that SGA is
able to achieve significant time and memory efficiency improvements while
maintaining considerable performance in the attack compared to other
state-of-the-art methods of attack.Comment: In submission to Journal, the codes are availiable at
https://github.com/EdisonLeeeee/GraphAd