Active Learning on Adversarially Corrupted Graphs

Marco Bressan, Nicolò Cesa-Bianchi, Tommaso d`Orsi +2

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} insi…