Papers › MONA: An Efficient and Scalable Strategy for Targeted k-Nodes Collapse
MONA: An Efficient and Scalable Strategy for Targeted k-Nodes Collapse
Yuqian Lv, Bo Zhou, Jinhuan Wang, Shanqing Yu, Qi Xuan
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The concept of k-core plays an important role in measuring the cohesiveness and engagement of a network. And recent studies have shown the vulnerability of k-core under adversarial attacks. However, there are few researchers concentrating on the vulnerability of individual nodes within k-core. Therefore, in this paper, we attempt to study Targeted k-Nodes Collapse Problem (TNsCP), which focuses on removing a minimal size set of edges to make multiple target k-nodes collapse. For this purpose, we first propose a novel algorithm named MOD for candidate reduction. Then we introduce an efficient strategy named MONA, based on MOD, to address TNsCP. Extensive experiments validate the effectiveness and scalability of MONA compared to several baselines. An open-source implementation is available at https://github.com/Yocenly/MONA.
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