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

Lionel Tabourier 1 Alina Stoica Fernando Peruani
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
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.
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Conference papers
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Submitted on : Thursday, February 11, 2016 - 5:24:44 PM
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Lionel Tabourier, Alina Stoica, Fernando Peruani. How to detect causality effects on large dynamical communication networks: A case study. Communication Systems and Networks (COMSNETS), Jan 2012, Bangalore, India. IEEE, Communication Systems and Networks (COMSNETS), pp.1-7, 〈10.1109/COMSNETS.2012.6151301〉. 〈hal-01273058〉

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