Understanding and Mitigating Vulnerabilities of Random Walks against Adversarial Attacks

Random walk-based algorithms are a key technique in many engineering applications, such as graph exploration, information diffusion, social network analysis, recommendation systems, machine learning, and biological network modeling, due to their adaptability, scalability, and simplicity. Although previous research has demonstrated that random walks exhibit vulnerability to certain link-rewiring attacks, a broader range of adversarial attack strategies remains largely unexplored. In this paper, we systematically investigate how diverse link deletion methods — bridge removal, hub removal, and exit blocking — combined with three link addition methods — triangle, loopback, and cluster trapping — impact the target node search (hitting time) and graph exploration (cover time) of the most standard types of random walk algorithms, such as Simple Random Walk, Degree-based Random Walk, and Memory-based Random Walk, across four types of graphs (Erdos and Rényi, Barabási-Albert (BA), voronoi, and regular) through simulation experiments. We also propose a mitigation strategy called the link reinforcement strategy, which strengthens the resilience of random walks against the aforementioned nine adversarial attacks. This paper highlights the weaknesses of random walk algorithms against adversarial attacks and presents an effective method to mitigate performance degradation caused by these attacks.