On the use of intrinsic time scale for dynamic community detection and visualization in social networks

Abstract : The analysis of social networks is a challenging research area, in particular because of their dynamic features. In this paper, we study such evolving graphs through the evolution of their community structure. More specifically, we build on existing approaches for the identification of stable communities over time. This paper presents two contributions. We first propose a new way to compute such stable communities, using a different time scale, called intrinsic time. This intrinsic time is related to the dynamics of the graph (e.g., in terms of link appearance or disappearance) and independent from traditional (extrinsic) time units, like the second. We then show how visualization both at intrinsic and extrinsic time scales can help validating and interpreting the obtained communities. Our results are illustrated on a social network made of contacts among the participants of the 2006 edition of the Infocom conference.
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Alice Albano, Jean-Loup Guillaume, Bénédicte Le Grand. On the use of intrinsic time scale for dynamic community detection and visualization in social networks. 8th IEEE International Conference on Research Challenges in Information Science (RCIS 2014), May 2014, Marrakech, Morocco. pp.1-11, ⟨10.1109/RCIS.2014.6861033⟩. ⟨hal-01208505⟩

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