Graphemes: self-organizing shape-based clustered structures for network visualisations

Network visualisations use clustering approaches to simplify the presentation of complex graph structures. We present a novel application of clustering algorithms, which controls the visual arrangement of the vertices in a cluster to explicitly encode information about that cluster. Our technique arranges parts of the graph into symbolic shapes, depending on the relative size of each cluster. Early results suggest that this layout augmentation helps viewers make sense of a graph’s scale and number of elements, while facilitating recall of graph features, and increasing stability in dynamic graph scenarios. Keywords Dynamic graphs, graph drawing, visual memory. ACM Classification Keywords H.5.0. Information interfaces and presentation (e.g., HCI): General. General Terms Human Factors, Theory.

• Type: Conference
• Year: 2010
• Where: CHI
• Authors: Ross Shannon, Aaron J. Quigley, Paddy Nixon
• Keywords: CHI 2010, Complex Graph Structures, Dynamic Graph Scenarios, Dynamic Graphs, Human Computer Interaction