arXiv:2508.02158v2 Announce Type: replace-cross Abstract: Detection of planted subgraphs in Erd\"os-R\'enyi random graphs has been extensively studied, leading to a rich body of results characterizing both statistical and computational thresholds. However, most prior work assumes a purely random generative model, making the resulting algorithms potentially fragile in the face of real-world perturbations. In this work, we initiate the study of semi-random models for the planted subgraph detection problem, wherein an adversary is allowed to remove edges outside the planted subgraph before the gr
This research addresses a critical gap in the robustness of AI algorithms, particularly those based on graph models, by moving beyond purely theoretical random models to semi-random, more adversarial conditions.
Improved robustness in algorithms for planted subgraph detection has implications for anomaly detection, cybersecurity, and potentially, the development of more resilient AI systems.
The focus shifting from ideal random models to semi-random adversarial models introduces a necessary practical dimension to algorithmic development, making future AI applications more robust against real-world perturbations.
- · AI algorithm developers
- · Cybersecurity sector
- · Data scientists working with complex networks
- · Adversarial actors attempting to hide anomalies
More robust graph-based algorithms for anomaly detection and pattern recognition will emerge.
This improved robustness could enhance the security and reliability of various AI-driven systems, from financial fraud detection to national security applications.
The methodology could influence broader AI research, making adversarial robustness a more central design principle across different AI paradigms.
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Read at arXiv cs.LG