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New active learning algorithm tackles adversarial graph corruption

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new active learning algorithm designed to identify corrupted vertices within graphs, even when adversaries tamper with network structures. The algorithm aims to efficiently find these hidden vertices using a minimal number of label queries. Its query complexity is polynomially dependent on the adversary's power and the graph's vertex expansion, a measure of connectivity. This work highlights the critical role of vertex expansion in active learning algorithms that are robust to structural adversarial attacks. AI

IMPACT This research could lead to more robust graph-based AI systems capable of detecting and mitigating adversarial manipulations.

RANK_REASON The cluster contains a research paper published on arXiv detailing a novel algorithm.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New active learning algorithm tackles adversarial graph corruption

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The cluster contains a research paper published on arXiv detailing a novel algorithm.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Marco Bressan, Nicol\`o Cesa-Bianchi, Tommaso d`Orsi, Emmanuel Esposito, Silvio Lattanzi ·

    Active Learning on Adversarially Corrupted Graphs

    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…

  2. arXiv stat.ML TIER_1 English(EN) · Silvio Lattanzi ·

    Active Learning on Adversarially Corrupted Graphs

    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…

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