Molecular model of dynamic social network based on e-mail communication
Bibliographic Data
| ID | 4635316 |
|---|---|
| Authors | Marcin Budka (0000-0003-0158-6309, Bournemouth University), Krzysztof Juszczyszyn (Wrocław University of Science and Technology, corresponding author), Katarzyna Musial (0000-0001-6038-7647, King's College - North Carolina), Anna Musial (0000-0001-9602-8929, Wrocław University of Science and Technology) |
| Year | 2013 |
| Volume | 3 |
| Issue | 3 |
| Pages | 543-563 |
| Publication date | 2013-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-013-0101-4 |
| OpenAlex | W1977959082 |
| Language | EN |
| Citations received | 1 |
| References cited | 33 |
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
Embedding · Euclidean space · Graph · Inference · Social media · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence · Theoretical Computer Science
Hidden order
Dynamical Processes on Complex Networks
Social Network Analysis
Multidimensional Scaling
The link‐prediction problem for social networks
Complex networks
Exploring complex networks
Collective dynamics of ‘small-world’ networks
Studying Online Social Networks
Zaller-Deffuant Model of Mass Opinion
Social network size in humans
Supervised methods for multi-relational link prediction
Temporal dynamics of communities in social bookmarking systems
Managing node disappearance based on information flow in social networks
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |