Name
Title | ||
|---|---|---|
How to detect causality effects on large dynamical communication networks: A case study |
Abstract | ||
|---|---|---|
Here we propose a set of dynamical measures to detect causality effects on communication datasets. Using appropriate comparison models, we are able to enumerate patterns containing causality relationships. This approach is illustrated on a large cellphone call dataset: we show that specific patterns such as short chain-like trees and directed loops are more frequent in real networks than in comparison models at short time scales. We argue that these patterns - which involve a node and its close neighborhood - constitute indirect evidence of active spreading of information only at a local level. This suggests that mobile phone networks are used almost exclusively to communicate information to a closed group of individuals. Furthermore, our study reveals that the bursty activity of the callers promotes larger patterns at small time scales. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/COMSNETS.2012.6151301 | Communication Systems and Networks |
Keywords | Field | DocType |
computer networks,mobile computing,mobile handsets,causality effect detection,causality relationships,communication datasets,directed loops,large cellphone call dataset,large dynamical communication networks,mobile phone networks,short chain-like trees | Mobile computing,Causality,Telecommunications network,Computer science,Real-time computing,Mobile phone,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-0297-5 | 4 | 0.44 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lionel Tabourier | 1 | 90 | 8.85 |
| Alina Stoica | 2 | 4 | 0.44 |
| Fernando Peruani | 3 | 27 | 3.51 |