Large-scale forecasting of information spreading

• Main Authors: Oksana Severiukhina, Sergey Kesarev, Klavdiya Bochenina, Alexander Boukhanovsky, Michael H. Lees, Peter M. A. Sloot
• Format: Article
• Language: English
• Published: SpringerOpen 2020-09-01
• Series: Journal of Big Data
• Subjects: Agent-based modelling; Model of information spread; Parallel simulation; Forecasting model; Data-driven model
• Online Access: http://link.springer.com/article/10.1186/s40537-020-00350-5

Abstract 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.