Adversarial Attack and Defense on Graph Data: A Survey

By Lichao Sun, Yingtong Dou, Ji Wang, Philip S. Yu, Bo Li. IEEE Transactions on Knowledge and Data Engineering, 2018

389 citations recorded by the source corpus.

Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works about adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph data due to its representation structure. Given the importance of graph analysis, an increasing number of studies over the past few years have attempted to analyze the robustness of machine learning models on graph data. Nevertheless, existing research considering adversarial behaviors on graph data often focuses on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation, which makes the comparison among different methods difficult. Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models. Moreover, we also compare different graph attacks and defenses along with their contributions and limitations, as well as summarize the evaluation metrics, datasets and future trends. We hope this survey can help fill the gap in the literature and facilitate further development of this promising new field1.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/48640ef269ffeb3e19c961de2eeca027a30841d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lichao Sun
2. Philip S. Yu
3. Carl Yang
4. Ke Zhang
5. Liang Chen
6. Gillian Dobbie
7. Nizar Bouguila
8. Saeed Rahmani

Citations in the corpus

• Deep Learning on Graphs: A Survey (2018, neutral): “Battaglia et al. [9] summarized how to use GNNs and GCNs for relational reasoning using a unified framework called graph networks, Lee et al. [10] reviewed the attention models for graphs, Zhang et al. [11] summarized some GCNs, and Sun et al. [12] briefly surveyed adversarial attacks on graphs.”
• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, background): “Sun et al. [16] reviewed strategies for adversarial learning on graph data.”
• Membership Inference Attacks on Machine Learning: A Survey (2021, background): “There are many surveys that summarize different attacks on ML models [35, 84, 114, 117, 137, 155, 166, 168, 176, 190, 228].”
• Secure Deep Graph Generation with Link Differential Privacy (2020, background): “Such concerns are rather urgent in the graph setting, where sensitive information can often be more easily compromised in a collective manner [Zhang et al., 2014], and privacy leakage can easily further propagate [Sun et al., 2018].”