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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 directions to improve comprehensibility through abstraction and thus reduce visual clutter. The experimental results demonstrate the effectiveness of our proposed method in clustering edges and providing good high level abstractions of complex graphs.