Studying Graph Dynamics Through Intrinsic Time Based Diffusion Analysis

Abstract : Complex networks may be studied in various ways, e.g., by analyzing the evolutions of their topologies over time, and in particular of their community structures. In this paper, we focus on another type of dynamics, related to diffusion processes on these networks. Indeed, our work aims at characterizing network dynamics from the diffusion point of view, and reciprocally, it evaluates the impact of graph dynamics on diffusion. We propose in this paper an innovative approach based on the 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 somehow isolate 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 three real datasets and show the promising results of intrinsic time-based diffusion analysis.
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Contributor : Alice Albano <>
Submitted on : Tuesday, September 2, 2014 - 9:57:49 AM
Last modification on : Sunday, January 19, 2020 - 6:38:32 PM


  • HAL Id : hal-01059850, version 1


Alice Albano, Jean-Loup Guillaume, Sébastien Heymann, Bénédicte Le Grand. Studying Graph Dynamics Through Intrinsic Time Based Diffusion Analysis. Applications of Social Media and Social Network Analysis, 2014. ⟨hal-01059850⟩



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