IES-Backbone: An Interactive Edge Selection Based Backbone Method for Small World Network Visualization

Visualization of the small world network is an excellent challenge for classic layout algorithm, which is highly connected, resulting in the shape of the hairball. Backbone extraction method can simplify the classic layout to get better visualization, and it has become a significant approach in this field. However, contemporary approaches have two primary defects. Centrality based method loses plenty of topology information, and most contemporary approaches have the problem in low interactivity due to parameter sensibility. We proposed a backbone method based on interactive edge selection (IES-Backbone) to solve two problems for small world network visualization that mentioned above. The proposed method starts with backbone extraction of the network and then apply the layout algorithm to get visualization results. A critical approach of the backbone method is edge selection, which is based on the distance between vertices layout of the binary stress model. Edge selection makes the simplified network a clear community structure feature with more topological details. The simplified network is high in homophily and has closer average path length to the original network. The visualization result is controlled by edge limit ratio $r$ and sampling rate $s$ . Different choices of two parameters can change the results substantially on visual without affecting the layout quality, which proves high interactivity for users. Experiments prove that IES-Backbone is an interactive visualization method that presents community and sufficient topological features.