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.
|Title of host publication||Unknown Host Publication|
|Editors||E Mynatt, D Schoner|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|Publication status||Published - 2010|
|Event||Proceedings of the 28th International Conference on Human Factors in Computing Systems - Atlanta, GA, USA|
Duration: 1 Jan 2010 → …
|Conference||Proceedings of the 28th International Conference on Human Factors in Computing Systems|
|Period||1/01/10 → …|
- Dynamic graphs
- graph drawing
- visual memory.
Shannon, R., Quigley, AJ., & Nixon, P. (2010). Graphemes: self-organizing shape-based clustered structures for network visualisations. In E. Mynatt, & D. Schoner (Eds.), Unknown Host Publication (pp. 4195-4200). Association for Computing Machinery. https://doi.org/10.1145/1753846.1754125