Name
Title | ||
|---|---|---|
Generating Multidimensional Social Network to Simulate the Propagation of Information. |
Abstract | ||
|---|---|---|
Social simulation implies two preconditions: determining a population and simulate the information diffusion within it. A population represents a group of interconnected individuals sharing information. In this paper, the population we generate is detailed by socio-cultural features, specifically the way that people tend to link together. To this end, the use of a social network is a little bit restrictive: people are linked by only one relationship. Multidimensional Social Networks (MSN) model 3D social networks where each dimension represent a kind of relationship [1]. The MSN architecture allows us to better represent the diversity of humans relations but also define distinctive rules for the simulation of the message diffusion. The inner idea is that information disseminates differently according to the links through which the information propagates. So, we present in this paper the modeling of our MSN based on social science and a simulation using propagation rules for each dimension. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2808797.2808870 | ASONAM |
Field | DocType | Citations |
Dynamic network analysis,Data mining,Population,Network generation,Architecture,Social network,Computer science,Cultural diversity,Social simulation,Complex network,Artificial intelligence,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.38 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathilde Forestier | 1 | 32 | 3.05 |
| Jean-Yves Bergier | 2 | 1 | 0.72 |
| Youssef Bouanan | 3 | 13 | 4.20 |
| Judicaël Ribault | 4 | 18 | 3.86 |
| Gregory Zacharewicz | 5 | 222 | 37.34 |
| Bruno Vallespir | 6 | 115 | 20.01 |
| Colette Faucher | 7 | 11 | 4.05 |