We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.

翻译：本研究探讨动态传播网络中的鲁棒影响力最大化问题。与近期研究一致，我们考虑网络在离散时间步中可能发生节点与边的增删场景，且影响力权重由相应节点特征与全局超参数共同决定。在此框架下，我们的目标是在每个时间步中，寻找能在超参数所有可能取值上实现最坏情况影响力传播最大化的种子节点集。我们提出一种结合乘性权重更新与贪心算法的近似解决方案，并给出可证明的质量保证。实验验证了所提方法的有效性与计算效率。