Molecular Model of Dynamic Social Network Based on E-mail communication.

Budka, M., Juszczyszyn, K., Musial, K. and Musial, A., 2013. Molecular Model of Dynamic Social Network Based on E-mail communication. Social Network Analysis and Mining.

Full text available as:

[img]
Preview
PDF
Budka2013a.pdf - Accepted Version

1MB

DOI: 10.1007/s13278-013-0101-4

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.

Item Type:Article
Subjects:UNSPECIFIED
Group:Faculty of Science and Technology
ID Code:20693
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:14 Feb 2013 08:19
Last Modified:10 Sep 2014 14:55

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -