This poster was presented at Computer Graphics & Visual Computing (CGVC24) at City St George's, University of London.
OSMAN AKBULUT, Xin Tong, Matthew Forshaw, Nicolas Holliman
Graph visualization of large and complex networks commonly suffers from clutter and overcrowding. Existing solutions typically prioritise structural simplification without consideration for underlying information content. In this paper, we present an approach which leverages Shanon entropy and edge weight variance to ensure the most interesting edges are retained. We demonstrate the applicability of the approach in a real-world use case.