Energy-Based Hierarchical Edge Clustering of Graphs

Effectively visualizing complex node-link graphs which depict relationships among data nodes is a challenging task due to the clutter and occlusion resulting from an excessive amount of edges. In this paper, we propose a novel energy-based hierarchical edge clustering method for node-link graphs. Taking into the consideration of the graph topology, our method first samples graph edges into segments using Delaunay triangulation to generate the control points, which are then hierarchically clustered by energy-based optimization. The edges are grouped according to their positions and ns to improve comprehensibility through abstraction and thus reduce visual clutter. The experimental results demonstrate the effectiveness of our proposed method in clustering edges and g good high level abstractions of complex graphs.

• Type: Conference
• Year: 2008
• Where: APVIS
• Authors: Hong Zhou, Xiaoru Yuan, Weiwei Cui, Huamin Qu, Baoquan Chen
• Keywords: APVIS 2008, Complex Node-link Graphs, Edge Clustering Method, Information Technology, Node-link Graphs