Eur. Phys. J. Special Topics 222, 1359-1376 (2013)
https://doi.org/10.1140/epjst/e2013-01931-y

Regular Article

Spreading processes on dynamically changing contact networks

¹ PetaByte Ltd, Bocskai utca 77-79, 1113 Budapest, Hungary
² AITIA International, Inc, Czetz János u. 48-50, 1039 Budapest, Hungary
³ Eötvös Loránd University, Pázmány Péter sétány 1/c, 1117 Budapest, Hungary
⁴ DFKI (German Research Institute for Artificial Intelligence), Trippstadter Strasse 122, 67663 Kaiserslautern, Germany

^a e-mail: lgulyas@aitia.ai
^b e-mail: kampis@petabyte-research.org

Received: 3 June 2013
Revised: 10 July 2013
Published online: 13 September 2013

Abstract

We develop and analyze an agent-based model for the study of information propagation in dynamic contact networks. We represent information as a state of a node in a network that can be probabilistically transferred to an adjacent node within a single time step. The model is based on a closed (yet sufficiently large) population that can support processes of link generation and annihilation using different contact regimes. Our study is confined to the case of homogeneous contacts, where each agent establishes and breaks contacts in the same way. We consider information to be available for spreading in a fixed time window (i.e. finite memory). We find, surprisingly, that information transmission (measured as the proportion of informed nodes after a fixed number of time steps) is identical for dynamic preferential and random networks, but radically different for the associate mixing contact regime. We also find that the probability of transmission is, similarly counterintuitively, not a main driver of the process as opposed the the main network par maters determining contact lifetime and the turnover rate on connections. We discuss the explanation and the significance of these results in the light of the fundamental difference between dynamic and static (cumulative) networks.