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New active learning algorithm tackles adversarial graph corruption

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel active learning algorithm designed to identify corrupted vertices within graphs, even when these graphs are tampered with by malicious actors. The algorithm aims to efficiently locate these corrupted subsets by minimizing label queries, with its effectiveness tied to the adversary's power and the graph's vertex expansion. This work marks the first instance where vertex expansion is identified as a critical factor in the query complexity of active learning algorithms that defend against structural adversarial attacks. AI

IMPACT Introduces a new method for robust graph analysis, potentially improving security in networked systems against adversarial manipulation.

RANK_REASON Academic paper published on arXiv detailing a new algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

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

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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