Adversarial Attacks on Node Embeddings via Graph Poisoning (1809.01093v3)

Abstract: The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable since they generalize to many models and are successful even when the attacker is restricted.

• The paper provides the first thorough analysis of unsupervised node embedding vulnerability to graph poisoning attacks and introduces efficient perturbation strategies using eigenvalue theory.
• The adversarial attacks designed are shown to be transferable, effectively degrading different node embedding models and highlighting a general vulnerability.
• The findings highlight a critical need for developing robust graph-based learning systems that can resist adversarial modifications to ensure reliability and security.

Analysis of Adversarial Attacks on Node Embeddings via Graph Poisoning

The paper "Adversarial Attacks on Node Embeddings via Graph Poisoning" by Aleksandar Bojchevski and Stephan Günnemann offers a robust paper of the vulnerability of node embeddings in graph-based machine learning models to adversarial attacks. It provides a comprehensive examination of node embeddings derived through unsupervised methods, especially those based on random walks, and analyzes their susceptibility to adversarial perturbations.

Summary

Node embeddings generated via unsupervised network representation learning have shown promising results in tasks like link prediction and node classification. However, this paper identifies a significant gap in the literature concerning the robustness of these embeddings against adversarial attacks. Specifically, the authors focus on adversarial perturbations at the network structure level, referred to as graph poisoning attacks.

The paper primarily targets random walk-based node embedding techniques, such as DeepWalk and node2vec, which are prevalent due to their capability to encode higher-order relational information. The authors introduce efficient strategies to perturb these node embeddings through algorithmically derived edge modifications in the network graph. The perturbations aim to degrade the quality of node embeddings and the performance of downstream tasks that rely on them.

Key Contributions

1. Adversarial Vulnerability Analysis: This is the first thorough analysis dedicated to understanding the susceptibility of unsupervised node embeddings, particularly those based on random walks, to adversarial attacks.
2. Efficient Perturbation Strategy: The authors derive adversarial perturbations by leveraging eigenvalue perturbation theory, which allows them to efficiently approximate and solve the bi-level optimization problem associated with poisoning attacks on node embeddings.
3. Transferability of Attacks: The paper demonstrates that the adversarial perturbations designed for one model can effectively extend to other node embedding models. This transferability suggests that specific graph manipulations can broadly affect unsupervised learning models, highlighting their potential vulnerability in various applications.
4. Restrictive Attack Consideration: The authors explore attack scenarios under restricted knowledge or capabilities, showcasing their method's effectiveness even when constraints limit the attacker's actions.

Implications and Future Directions

The findings have significant implications for the future development and deployment of graph-based learning systems. In particular, they stress the critical need for creating robust methods that can resist such adversarial modifications to ensure reliability and security, especially in sensitive or operational environments. The paper suggests that further exploration into mitigation strategies, such as adversarial training or robust graph structure methods, is vital.

Speculatively, the results could hasten the development of more sophisticated adversarial defenses in unsupervised learning. Additionally, understanding the graph structure's role in these attacks might lead to innovations that could render these models less vulnerable or even resistant to such perturbations. Given that node embeddings are often utilized in social networks, recommendation systems, and bioinformatics, ensuring their integrity against adversarial influences is of paramount concern for these applications.

Conclusion

This research uncovers and demonstrates a decisive vulnerability in unsupervised node embeddings, encouraging further developments in making these models more resilient to adversarial attacks. The paper not only contributes by undermining the current robustness assumptions of widespread node embedding techniques but also by laying groundwork for future research into adversarial paradynamics within graph-structured data domains.