Understanding community evolution in Complex systems science

Qinna Wang (1) , Eric Fleury (1, 2, 3)

1 DNET - Dynamic Networks
2 IXXI - Institut Rhône-Alpin des systèmes complexes
3 LIP - Laboratoire de l'Informatique du Parallélisme

Complex systems is a new approach in science that studying organized be- haviours in computer science, biology, physics, chemistry, and many other fields. By collecting articles containing topic keywords relevant for the field of complex networks from ISI Web of knowledge during 1985-2009, we construct a science network, which connects ~ 215000 articles according to the proportion of shared references. Moreover, articles' publication time makes it dynamically evolve in time. We here use a two-step approach [3] to explore community evolution and study underlying information behind community changes. We firstly detect com- munities by applying Louvain algorithm [2] on each snapshot graph, and secondly construct relationships between partitions at successive snapshot graphs [1].

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Algorithme et structure de données [cs.DS]

Qinna Wang, Eric Fleury. Understanding community evolution in Complex systems science. DYNAM: 1st International Workshop on Dynamicity, Dec 2011, Toulouse, France. pp.12. ⟨hal-00725094⟩