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Hypernetwork Dismantling via Deep Reinforcement Learning
[article]
2022
arXiv
pre-print
Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes. It has been widely adopted in many real-world applications such as epidemic control and rumor containment. However, conventional methods usually focus on simple network modeling with only pairwise interactions, while group-wise interactions modeled by hypernetwork are ubiquitous and critical. In this work, we formulate the hypernetwork dismantling problem as a node sequence decision problem
arXiv:2104.14332v2
fatcat:ob7txofxzffgvlbed6c3r3ekm4