研究人员开发了一种新颖的主动学习算法，旨在识别图中的损坏顶点，即使这些图被恶意行为者篡改。该算法旨在通过最小化标签查询来有效定位这些损坏的子集，其有效性取决于对手的能力和图的顶点扩展。这项工作首次将顶点扩展确定为主动学习算法在防御结构性对抗攻击时的查询复杂度的关键因素。 AI
影响 引入了一种新的鲁棒图分析方法，有可能提高网络系统在对抗性操纵下的安全性。
排序理由 学术论文发布在arXiv上，详细介绍了一种新算法。[lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.04869v1 Announce Type: cross Abstract: Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} inside a graph $G^*$. To this end, the adversary can a…
Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} inside a graph $G^*$. To this end, the adversary can add edges between the corrupted vertices, as well a…