It is important to reconstruct the hidden network structure from the infection status change of an information propagation process for evidence-based spatial decision-making. Unlike previous work, we not only consider the heterogeneity of the propagation agents, but also incorporate the heterogeneity of the text contents of information within the propagation process. In addition, the infection status is no longer restricted to the binary type (infected or not), and we allow the number of pieces of information texts to be counted which represents the degree of infection. The resulting model is a network-based multivariate recurrent event model, in which the interactions between different types of text, between different agents, between agents and text types, and their mutual impacts on the whole propagation process can be comprehensively investigated. On that basis, a nonparametric mean-field equation is derived to govern the propagation process, and a compressive sensing algorithm is provided to infer the hidden spatial propagation network from the infection status data. Finally, the proposed methodology is tested through synthetic data and a real data set of information diffusion on Twitter.

中文翻译：

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重构具有异构代理和文本内容的空间信息传播网络

从信息传播过程的感染状态变化重建隐藏网络结构对于基于证据的空间决策非常重要。与之前的工作不同，我们不仅考虑了传播代理的异质性，还考虑了传播过程中信息文本内容的异质性。此外，感染状态不再局限于二进制类型（感染与否），我们允许统计代表感染程度的信息文本的数量。由此产生的模型是一个基于网络的多元循环事件模型，其中不同类型文本之间、不同代理之间、代理与文本类型之间的交互，可以全面调查它们对整个传播过程的相互影响。在此基础上，推导出非参数平均场方程来控制传播过程，并提供压缩感知算法从感染状态数据推断隐藏的空间传播网络。最后，通过合成数据和 Twitter 上信息传播的真实数据集对所提出的方法进行了测试。