Unifying information propagation models on networks and influence maximization,Physical Review E

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Unifying information propagation models on networks and influence maximization
Physical Review E ( IF 2.2 ) Pub Date : 2022-09-16 , DOI: 10.1103/physreve.106.034316
Yu Tian ₁ , Renaud Lambiotte ₁

Affiliation

Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalize them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close to optimal performance of the customized direct search numerically on both synthetic and real networks.

中文翻译：

统一网络上的信息传播模型和影响最大化

网络上的信息传播是社会、行为和经济科学的中心主题，具有重要的理论和实践意义，例如病毒式营销的影响力最大化问题。在这里，我们考虑一个统一经典独立级联模型和线性阈值模型的模型，并通过考虑连续变量和允许动态反馈来推广它们。然后我们将其影响最大化公式化为一个混合整数非线性规划问题，并采用无导数方法。此外，我们证明了该问题可以在线性动力学的特殊情况下得到精确解决，其中选择标准与 Katz 中心性密切相关，并提出了一种具有局部收敛性的定制直接搜索方法。

更新日期：2022-09-16

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