Modeling continuous-time information diffusion process on complex networks

社交网络可以发挥重要作用，作为传播各种信息的媒介，包括创新、热点话题甚至恶意谣言。在这项研究中，我们利用机器学习方法构建了复杂网络中信息扩散现象的可预测模型。此外，我们还提出并评估了模型的各种应用，这些模型提出了在给定约束条件下增加和减少信息传播的方法，并分析了信息传播中节点的行为。

A social network can play an important role as a medium for the spread of various types of information, including innovation, hot topics and even malicious rumors. In this research, we have constructed predictable models for information diffusion phenomena in complex networks by exploiting a machine learning approach. Moreover, we have proposed and evaluated such a variety of applications of the models that present the methods of increasing and decreasing the information spread under given constraints, and analyzing behaviors of nodes in information propagation.