Cluster dynamics in the collapsing Soviet shipping network

Rawya Zreik (1) , César Ducruet (2, 3) , Charles Bouveyron (1) , Pierre Latouche (4)

1 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
2 GC (UMR_8504) - Géographie-cités
3 CNRS - Centre National de la Recherche Scientifique
4 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

In recent years, many clustering methods have been proposed to extract information from networks. The principle is to look for groups of vertices with homogenous connection profiles. Most of these techniques are suitable for static networks, that is to say, not taking into account the temporal dimension. This work is motivated by the need of analyzing evolving networks where a decomposition of the networks into subgraphs is given. Therefore, in this chapter, we consider the random subgraph model (RSM) which was proposed recently to model networks through latent clusters built within known partitions. Using a state space model to characterize the cluster proportions, RSM is then extended in order to deal with dynamic networks. We call the latter the dynamic random subgraph model (dRSM). A variational expectation maximization (VEM) algorithm is proposed to perform inference. The case of USSR collapse was chosen for testing our method and check whether a major geopolitical change affected the evolution of clusters over time, and where. Main results point to an impact felt mostly at secondary ports and in particular regions (Europe, Atlantic) in 1990-1991, and at places being already specialized in socialist trades.

Mots clés: COUV; PARIS team

Domaines: Géographie

Rawya Zreik, César Ducruet, Charles Bouveyron, Pierre Latouche. Cluster dynamics in the collapsing Soviet shipping network. César Ducruet. Advances in Shipping Data Analysis and Modeling. Tracking and Mapping Maritime Flows in the Age of Big Data, Routledge, pp.317-337, 2017, Routledge Studies in Transport Analysis, 9781138280939. ⟨hal-01623593⟩