Adversarial Attack on Network Embeddings via Supervised Network Poisoning
This addresses robustness issues in network representation learning for applications such as social network analysis, though it is incremental as it builds on existing poisoning methods.
The paper tackles the problem of adversarial attacks on network embeddings by proposing VIKING, a supervised network poisoning strategy that degrades performance on downstream tasks like node classification and link prediction, achieving up to 18% improvement over state-of-the-art methods.
Learning low-level node embeddings using techniques from network representation learning is useful for solving downstream tasks such as node classification and link prediction. An important consideration in such applications is the robustness of the embedding algorithms against adversarial attacks, which can be examined by performing perturbation on the original network. An efficient perturbation technique can degrade the performance of network embeddings on downstream tasks. In this paper, we study network embedding algorithms from an adversarial point of view and observe the effect of poisoning the network on downstream tasks. We propose VIKING, a supervised network poisoning strategy that outperforms the state-of-the-art poisoning methods by upto 18% on the original network structure. We also extend VIKING to a semi-supervised attack setting and show that it is comparable to its supervised counterpart.