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Robust Sequence Networked Submodular Maximization

Qihao ShiBingyang FuCan Wang ...+3 Chun Chen
Dec 2022
摘要
In this paper, we study the \underline{R}obust \underline{o}ptimization for\underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization(RoseNets) problem. We interweave the robust optimization with the sequencenetworked submodular maximization. The elements are connected by a directedacyclic graph and the objective function is not submodular on the elements buton the edges in the graph. Under such networked submodular scenario, the impactof removing an element from a sequence depends both on its position in thesequence and in the network. This makes the existing robust algorithmsinapplicable. In this paper, we take the first step to study the RoseNetsproblem. We design a robust greedy algorithm, which is robust against theremoval of an arbitrary subset of the selected elements. The approximationratio of the algorithm depends both on the number of the removed elements andthe network topology. We further conduct experiments on real applications ofrecommendation and link prediction. The experimental results demonstrate theeffectiveness of the proposed algorithm.
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