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 sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithm, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.

翻译：在本文中, 我们研究下线 {R} obust \ sunderline {sunderline} sunderline} {underline} Net} 工作 \ underline} ummodal 最大化( RoseNets) 问题 。 我们用序列网络子模块最大化( RoseNets) 将强力优化结合。 元素由定向的单流图连接, 客观函数不是对元素的子模块, 而是在图形的边缘 。 在这种网络子模块的假设下, 从序列中移除元素的影响既取决于其在序列和网络中的位置。 这使得现有的强力算法无法适用。 在本文中, 我们首先研究罗斯Nets问题。 我们设计了强大的贪婪算法, 以抵御任意清除选定元素的子集。 算法的近似率取决于被删除元素的数量和网络表层。 我们进一步对建议的真实应用和链接预测进行实验。 实验结果显示了提议的算法的有效性 。