Inicio  /  Information  /  Vol: 12 Par: 3 (2021)  /  Artículo
ARTÍCULO
TITULO

Modeling of Information Processes in Social Networks

Sergey Yablochnikov    
Mikhail Kuptsov and Maksim Mahiboroda    

Resumen

In order to model information dissemination in social networks, a special methodology of sampling statistical data formation has been implemented. The probability distribution laws of various characteristics of personal and group accounts in four social networks are investigated. Stochastic aspects of interrelations between these indicators were analyzed. The classification of groups of social network users is proposed, and their characteristic features and main empirical regularities of mutual transitions are marked. Regression models of forecasting changes in the number of users of the selected groups have been obtained.

 Artículos similares

       
 
Louis Closson, Christophe Cérin, Didier Donsez and Jean-Luc Baudouin    
This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the ac... ver más
Revista: Information

 
Emilio Matricciani    
We propose that short-term memory (STM), when processing a sentence, uses two independent units in series. The clues for conjecturing this model emerge from studying many novels from Italian and English Literature. This simple model, referring to the sur... ver más
Revista: Information

 
Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, Natalya Shepeta, Dmitry Malyshev, Aleksey Popov and Lev Kazakovtsev    
Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network de... ver más
Revista: Algorithms

 
Dibo Dong, Shangwei Wang, Qiaoying Guo, Yiting Ding, Xing Li and Zicheng You    
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network?long short-term memory joint fr... ver más

 
Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang     Pág. 115 - 142
Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the so... ver más