A Matter of Time – Intrinsic or Extrinsic – for Diffusion in Evolving Complex Networks

Alice Albano, Jean-Loup Guillaume, Sébastien Heymann and Bénédicte Le Grand

Proceedings of the 2013 IEEE/ACM International Conference on Advances  n Social Networks Analysis and Mining (ASONAM 2013), Niagara Falls, Canada

Diffusion phenomena occur in many kinds of real-world complex networks, e.g., biological, information or social networks. Because of this diversity, several types of diffusion models have been proposed in the literature: epidemiological models, threshold models, innovation adoption models, among others. Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks. In order to study the impact of graph dynamics on diffusion, we propose in this paper an innovative approach based on a notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to isolate somehow the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on synthetic graphs, as well as on a dataset extracted from the Github sofware sharing platform.


Community Cores in Evolving Networks

Massoud Seifi and Jean-Loup Guillaume

Proceedings of the Mining Social Network Dynamic 2012 Workshop (MSND), Inconjunction with the international conference World Wide Web WWW 2012, Lyon,France, pp. 1173-1180

Community structure is a key property of complex networks.Many algorithms have been proposed to automatically detect communities in static networks but few studies haveconsidered the detection and tracking of communities in anevolving network. Tracking the evolution of a given community over time requires a clustering algorithm that producesstable clusters. However, most community detection algorithms are very unstable and therefore unusable for evolvingnetworks. In this paper, we apply the methodology proposedin [14] to detect what we call community cores in evolvingnetworks. We show that cores are much more stable than »classical » communities and that we can overcome the disadvantages of the stabilized methods.