This research proposes a system based on a combination of various components for parallel modelling and forecasting the processes in networks with data assimilation from the real network. The main novelty of this work consists of the assimilation of data for forecasting the processes in social networks which allows improving the quality of the forecast. The social network VK was considered as a source of information for determining types of entities and the parameters of the model. The main component is the model based on a combination of internal sub-models for more realistic reproduction of processes on micro (for single information message) and meso (for series of messages) levels. Moreover, the results of the forecast must not lose their relevance during the calculations. In order to get the result of the forecast for networks with millions of nodes in reasonable time, the process of simulation has been parallelized. The accuracy of the forecast is estimated by MAPE, MAE metrics for micro-scale, the Kolmogorov–Smirnov criterion for aggregated dynamics. The quality in the operational regime is also estimated by the number of batches with assimilated data to achieve the required accuracy and the ratio of calculation time in the frames of the forecasting period. In addition, the results include experimental studies of functional characteristics, scalability, as well as the performance of the system.

中文翻译：

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信息传播的大规模预测

这项研究提出了一个基于各种组件的组合的系统，用于并行建模和预测网络中的过程，并与真实网络进行数据同化。这项工作的主要新颖之处在于吸收了社交网络中用于预测过程的数据，从而可以提高预测的质量。社交网络VK被视为确定实体类型和模型参数的信息源。主要组件是基于内部子模型的组合的模型，用于在微观（对于单个信息消息）和中观（对于消息系列）级别上更真实地重现过程。此外，预测结果在计算过程中一定不能失去其相关性。为了在合理的时间内获得具有数百万个节点的网络的预测结果，对仿真过程进行了并行化。预测的准确性由MAPE，MAE微观尺度，Kolmogorov-Smirnov聚集动力学准则估计。还可以通过使用同化数据的批次数量来估算操作方案中的质量，以达到所需的精度，并在预测周期的框架内计算时间的比例。此外，结果还包括功能特性，可伸缩性以及系统性能的实验研究。还可以通过使用同化数据的批次数量来估算操作方案中的质量，以达到所需的精度，并在预测期的框架内计算时间的比例。此外，结果还包括功能特性，可伸缩性以及系统性能的实验研究。还可以通过使用同化数据的批次数量来估算操作方案中的质量，以达到所需的精度，并在预测期的框架内计算时间的比例。此外，结果还包括功能特性，可伸缩性以及系统性能的实验研究。