Abstract
In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain.
Highlights
The emergence of complex behaviour in a system composed of many interacting elements is one of the most fascinating phenomena and recently a prominent area of research
Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model
We argue that, in the presence of the data coming from real system, the verification of such a model should be done by means of checking if it is possible to recreate the social network from simulation results and compare it with the real network structures which have evolved in the period of time covered by the simulation
Summary
The emergence of complex behaviour in a system composed of many interacting elements is one of the most fascinating phenomena and recently a prominent area of research. There are many types of complex networked systems, which can be classified in many different ways. One of the approaches distinguishes infrastructural (Internet, WWW, energy and transportation networks) and natural complex systems (biological networks, social systems and ecosystems) (Barrat et al 2008). Another classification divides complex networks into technological, social, biological and information networks (Kolaczyk 2009). The number of nodes in these networks can differ from hundreds to millions (Watts and Strogatz 1998). One of the challenges is to identify which component influences the behaviour of other components, which is directly connected with the dynamics of such structures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
