logo EDITE Daniel BERNARDES
Identité
Daniel BERNARDES
État académique
Thèse soutenue le 2014-03-21
Sujet: Phénomènes de diffusion sur les grands réseaux : mesure et analyse pour la modélisation.
Direction de thèse:
Encadrement de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:hal-00857518
Inadequacy of SIR Model to Reproduce Key Properties of Real-world Spreading Phenomena: Experiments on a Large-scale P2P System
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 reach this goal. We reconstruct the underlying social network of peers sharing content and perform simulations on it to assess the relevance of the standard SIR model to mimic key properties of real spreading cascades. First, we examine the impact of the network topology on observed properties. Then we turn to the evaluation of two heterogeneous extensions of the SIR model. Finally, we improve the social network reconstruction, introducing an affinity index between peers, and simulate a SIR model which integrates this new feature. We conclude that the simple, homogeneous model is insufficient to mimic real spreading cascades. Moreover, none of the natural extensions of the model we considered, which take into account extra topological properties, yielded satisfying results in our context. This raises an alert against the careless, widespread use of this model.
Social Network Analysis and Mining (SNAM)article in peer-reviewed journal 2013-06-28
oai:hal.archives-ouvertes.fr:hal-00690606
Examining Key Properties of Diffusion Models for Large-Scale Real-World Networks
Understanding the spread of information on complex networks is a key issue from a theoretical and applied perspective. Despite the large effort in developing models for this phenomenon, gauging them with large-scale real-world data remains an important obstacle in the field. In this work we assess the relevance of the classic SIR model to capture key properties of spreading phenomena in real communication networks. We use a real file spreading trace in a P2P network to calibrate the model and to simulate similar diffusions. Comparing spreading cascades of real and simulated traces we observe sharp topological differences and conclude that this model fail to mimic key properties of such cascades.
preprint 2012-02-01
oai:hal.archives-ouvertes.fr:hal-00730538
Relevance of SIR Model for Real-world Spreading Phenomena: Experiments on a Large-scale P2P System
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 real spreading cascades. We examine the impact of the network topology on observed properties and finally turn to the evaluation of two heterogeneous extensions of the SIR model. We conclude that all the models tested failed to reproduce key properties of such cascades: real spreading cascades are relatively ''elongated'' compared to simulated ones. We have also observed some interesting similarities common to all SIR models tested.
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE/ACM International Conference on Advances in Social Networks Analysis and Miningproceeding with peer review 2012-08-26
Soutenance
Thèse: Information diffusion in Complex Networks : measurement-based analysis applied to modelling
Soutenance: 2014-03-21
Rapporteurs: Eric FLEURY    Marc TOMMASI