In this chapter we introduce a model for analyzing the spread of epidemics in a disconnected mobile network. The work is based on an extension, to a dynamic setting, of the eigenvector centrality principle introduced by two of the authors for the case of static networks. The extension builds on a new definition of connectivity matrix for a highly partitioned mobile system, where the connectivity between a pair of nodes is defined as the number of contacts taking place over a finite time window. The connectivity matrix is then used to evaluate the eigenvector centrality of the various nodes. Numerical results from real-world traces are presented and discussed. The applicability of the proposed approach to select on-line message forwarders is also addressed.
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Carreras, I., Miorandi, D., Canright, G.S., Engø-Monsen, K. (2007). Eigenvector Centrality in Highly Partitioned Mobile Networks: Principles and Applications. In: Dressler, F., Carreras, I. (eds) Advances in Biologically Inspired Information Systems. Studies in Computational Intelligence, vol 69. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72693-7_7
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