The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting nodes. If we can select nodes that contribute to large-scale diffusion, the nodes are expected to be important for viral marketing. Given a network and the size of the starting nodes, the problem of selecting nodes for maximizing information diffusion is called influence maximization problem. We propose three new approximation methods (Dynamic Degree Discount, Dynamic CI, and Dynamic RIS) for influence maximization problem in dynamic networks. These methods are the extensions of previous methods for static networks to dynamic networks. When compared with the previous methods, MC Greedy and Osawa, our proposed methods were found better than the previous methods: Although the performance of MC greedy was better than the three methods, it was computationally expensive and intractable for large-scale networks. The computational time of our proposed methods was more than 10 times faster than MC greedy, so they can be computed in realistic time even for large-scale dynamic networks. When compared with Osawa, the performances of these three methods were almost the same as Osawa, but they were approximately 7.8 times faster than Osawa. Based on these facts, the proposed methods are suitable for influence maximization in dynamic networks. Finding the strategies of choosing a suitable method for a given dynamic network is practically important. It is a challenging open question and is left for our future work. The problem of adjusting the parameters for Dynamic CI and Dynamic RIS is also left for our future work.

中文翻译：

---

动态网络中影响最大化的扩展方法。

谣言在人与人之间传播的过程可以用社交网络中的信息传播来表示。谣言传播的规模因起始节点而异。如果我们可以选择有助于大规模传播的节点，那么这些节点有望成为病毒式营销的重要节点。给定网络和起始节点的大小，为最大化信息扩散选择节点的问题称为影响最大化问题。我们针对动态网络中的影响最大化问题提出了三种新的近似方法（动态度数折扣、动态 CI 和动态 RIS）。这些方法是先前静态网络方法对动态网络的扩展。与之前的方法 MC Greedy 和 Osawa 相比，我们提出的方法比之前的方法更好：虽然 MC greedy 的性能优于这三种方法，但对于大规模网络来说，它的计算量大且难以处理。我们提出的方法的计算时间比 MC 贪婪法快 10 倍以上，因此即使对于大规模动态网络，它们也可以实时计算。与 Osawa 相比，这三种方法的性能与 Osawa 几乎相同，但比 Osawa 快约 7.8 倍。基于这些事实，所提出的方法适用于动态网络中的影响最大化。找到为给定动态网络选择合适方法的策略实际上很重要。这是一个具有挑战性的开放性问题，留给我们未来的工作。动态 CI 和动态 RIS 的参数调整问题也留给我们以后的工作。