PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generative models for rapid information propagation
Kirill Dyagilev, Kirill Dyagilev, Shie Mannor and Elad Yom-Tov
In: Workshop on Social Media Analytics, June 25, 2010, Washington DC, U.S.A..

Abstract

We consider the dynamics of rapid propagation of information in complex social networks focusing on mobile phone networks. We introduce two models for an information propagation process. The first model describes the temporal behavior of people which leads to the emergence of information propagation events and is based on the existence of two types of subscribers: regular subscribers and subscribers that tend to spread information. The second model describes the topology of paths in which the information propagates from one subscriber to another. We further introduce an efficient algorithm for identification of information propagation events. We then apply our algorithm to a large-scale mobile phone network and demonstrate the correspondence between theoretical expectations and the actual results.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:7755
Deposited By:Kirill Dyagilev
Deposited On:17 March 2011