Adversarial Influence Maximization
Adversarial Influence Maximization
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DOI：
10.1109/isit.2019.8849828
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发表时间：
2016-11
期刊：
影响因子：
--
通讯作者：
Justin Khim;Varun Jog;Po-Ling Loh
中科院分区：
文献类型：
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作者：
Justin Khim;Varun Jog;Po-Ling Loh
我们考虑了对抗环境下传染模型在固定网络中的影响最大化问题。目标是选择一组最优节点来播种影响过程，以便在活动结束时受影响的节点数量尽可能大。我们将这一问题描述为玩家与对手之间的重复博弈，对手指定感染可能传播的边缘，玩家选择以在线方式影响的节点集。在无向网络和有向网络中建立了极大极小伪后悔的上界和下界。
We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible. We formulate the problem as a repeated game between a player and adversary, where the adversary specifies the edges along which the contagion may spread, and the player chooses sets of nodes to influence in an online fashion. We establish upper and lower bounds on the minimax pseudo-regret in both undirected and directed networks.