| dc.contributor.author | Guchev, Vladimir | en_US |
| dc.contributor.author | Angelini, S. | en_US |
| dc.contributor.author | Amati, G. | en_US |
| dc.contributor.editor | Tobias Isenberg and Filip Sadlo | en_US |
| dc.date.accessioned | 2016-06-09T09:33:37Z | |
| dc.date.available | 2016-06-09T09:33:37Z | |
| dc.date.issued | 2016 | en_US |
| dc.identifier.isbn | 978-3-03868-015-4 | en_US |
| dc.identifier.issn | - | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/eurp.20161147 | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10 | |
| dc.description.abstract | Tasks associated with the investigation of large complex clustered networks are widespread in various research areas. Among the popular and common approaches to exploratory analysis, it is definitely worthwhile to underscore the node-link-based graph visualization. However, despite the prevalence of node-link-based tools, its graphic design and geometric representation of topology almost invariably formed by a spontaneous spatial structure, or on the contrary, by a too rigidly ordered arrangement. Transformation possibilities of multivariate data structures may allow finding a suitable graphic balance between optic chaos and visual primitiveness by the use of partially ordered sets for grouping. By taking the studying of Twitter communities as a task, the paper presents a data modelling method in conjunction with a set of visualization techniques, which implement a convenient and perceivable interactive toolset for analytical exploration of overlapping network clusters. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Understanding Networks beyond Overlapping Clusters | en_US |
| dc.description.seriesinformation | EuroVis 2016 - Posters | en_US |
| dc.description.sectionheaders | Poster | en_US |
| dc.identifier.doi | 10.2312/eurp.20161147 | en_US |
| dc.identifier.pages | 81-83 | en_US |
