Name
Abstract | ||
|---|---|---|
Social interactions vary in time and appear to be driven by intrinsic mechanisms that
shape the emergent structure of social networks. Large-scale empirical observations of
social interaction structure have become possible only recently, and modelling their
dynamics is an actual challenge. Here we propose a temporal network model which builds on
the framework of activity-driven time-varying networks with memory. The
model integrates key mechanisms that drive the formation of social ties – social
reinforcement, focal closure and cyclic
closure, which have been shown to give rise to community structure and
small-world connectedness in social networks. We compare the proposed model with a
real-world time-varying network of mobile phone communication, and show that they share
several characteristics from heterogeneous degrees and weights to rich community
structure. Further, the strong and weak ties that emerge from the model follow similar
weight-topology correlations as real-world social networks, including the role of weak
ties. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1140/epjb/e2015-60481-x | European Physical Journal B |
Keywords | Field | DocType |
Link Weight, Temporal Network, Egocentric Network, Nabu, Triadic Closure | Dynamic network analysis,Social relation,Data science,Social connectedness,Social network,Evolving networks,Triadic closure,Network model,Condensed matter physics,Interpersonal ties,Physics | Journal |
Volume | Issue | ISSN |
abs/1506.00393 | 11 | 1434-6036 |
Citations | PageRank | References |
14 | 0.65 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillaume J. Laurent | 1 | 26 | 4.01 |
| Jari Saramaki | 2 | 41 | 4.41 |
| Márton Karsai | 3 | 422 | 30.42 |