How to detect causality effects on large dynamical communication networks: A case study

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.