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Visualising the Evolution of Dynamic Communities in Social Networks using Timelines

Publication Type: 
Refereed Conference Meeting Proceeding
Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches for detecting communities mostly focus on identifying them in static graphs. Researchers often consider the problem of tracking the evolution of communities in dynamic scenarios. In this work we present the \emph{Dynamic Community Viewer} (DCV), a visualisation tool for tracking the life cycle of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. The DCV is capable of visualising the development of these events at different points in time using browsable timelines. Specifically, it can visualise the birth, death, split, merge, contraction, expansion and any user-defined attribute change (e.g. topics) as evolutionary events for sets of dynamic communities. Our tool is based on an established community tracking model that leverages a community-matching strategy for efficiently identifying and tracking dynamic communities.
Conference Name: 
2nd Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) in ECML/PKDD 2018
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Research Group: 
National University of Ireland, Galway (NUIG)
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