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.
Daniel F. Bernardes, Matthieu Latapy, Fabien Tarissan
Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), Istanbul, Turkey
Understanding the spread of information on complex networks is a key issue from a theoretical and applied perspective. Despite the effort in developing theoretical models for this phenomenon, gauging them with large-scale real-world data remains an important challenge due to the scarcity of open, extensive and detailed data. In this paper, we explain how traces of peer-to-peer file sharing may be used to this goal. We also perform simulations to assess the relevance of the standard SIR model to mimic key properties of spreading cascade. We examine the impact of the network topology on observed properties and finally turn to the evaluation of two heterogeneous versions of the SIR model. We conclude that all the models tested failed to reproduce key properties of such cascades: typically real spreading cascades are relatively “elongated” compared to simulated ones. We have also observed some interesting similarities common to all SIR models tested.
Alice Albano, Jean-Loup Guillaume, and Bénédicte Le Grand
Proceedings of the Mining Social Network Dynamic 2012 Workshop (MSND), In conjunction with the international conference World Wide Web WWW 2012, Lyon, France
Many studies have been made on diffusion in the field of epidemiology, and in the last few years, the development of social networking has induced new types of diffusion. In this paper, we focus on file diffusion on a peer-to-peer dynamic network using eDonkey protocol. On this network, we observe a linear behavior of the actual file diffusion. This result is interesting, because most diffusion models exhibit exponential behaviors. In this paper, we propose a new model of diffusion, based on the SI (Susceptible / Infected) model, which produces results close to the linear behavior of the observed diffusion. We then justify the linearity of this model, and we study its behavior in more details.
Les phénomènes de diffusion sont présents dans de nombreux contextes: diffusion d’épidémies, de virus informatiques, d’information dans des réseaux sociaux, etc. Bien que les réseaux où se produit la diffusion soient souvent dynamiques, cette dynamique n’est pas prise en compte dans la plupart des modèles existants. L’objectif de ces travaux est de proposer des modèles de diffusion, et d’étudier l’impact de la dynamique du réseau sur la diffusion.