ClusterVis: Visualizing Nodes Attributes in Multivariate Graphs

Abstract : Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are usually based on multivariate graphs, i.e., graphs in which items and relationships have multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of the items' attributes. The integration of these two components (topology and multiple attributes) in a single visualization turns into a challenge due to the necessity of simultaneously representing the connections and mapping attributes possibly generating overlapping elements. Among usual strategies to overcome this legibility problem we find filtering and aggregation that makes possible a simplified representation with reduced size and density providing a general view. However, this simplification may lead to a reduction of the amount of information being displayed, while in several applications the graph details still need to be represented in order to make possible in-depth data analysis. In face of that, we propose ClusterVis, a visualization technique aiming at exploring nodes attributes pertaining to sub-graphs, which are either obtained from clustering algorithms or some user-defined criteria. The technique allows comparing attributes of nodes while keeping the representation of the relationships among them. The technique was implemented within a visualization framework and evaluated by potential users.
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Ricardo Cava, Carla Freitas, Marco Winckler. ClusterVis: Visualizing Nodes Attributes in Multivariate Graphs. Proceedings of the Symposium on Applied Computing, Apr 2017, Marrakech, Morocco. ⟨hal-02146032⟩

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