Abstract
Graph Neural Networks (GNNs) have various applications in real-life scenarios. However, they are vulnerable to adversarial attacks, especially Graph Injection Attacks (GIAs). The flexible and high-risk GIAs pose a significant threat by injecting malicious nodes into the graph. Regrettably, defense strategies to resist GIAs are still scarce. The current defenders lack comprehensive strategies that can effectively resist a wide range of injection techniques. Particularly, when confronted with highly unnoticeable GIAs, the performance of these defenses is significantly diminished. In this paper, we propose a simple and effective GIA defense method named Faithful INjection DefendER (FINDER). FINDER evaluates the impact of the nodes on their neighbors in the context of message aggregation to identify the potential injected nodes in the graph. By blocking identified injected nodes from interacting with the benign nodes, FINDER prevents the further propagation of misinformation and the spread of adversarial influence throughout the graph. Through extensive experiments conducted on three benchmarks, FINDER demonstrates remarkable performance in accurately identifying injected nodes and defending various GIAs.