A Graph Layout Framework Combining t-Distributed Neighbor Retrieval Visualizer and Energy Models

Abstract

Graph layout investigates the structure of the graph in order to better obtain the information implied in the graph. To solve the shortcomings of dimension reduction layouts on local adjustment and the insufficiency of energy models to maintain the overall structure of the graphs, this paper proposes a new graph layout framework called “tNEM” that layouts graphs by combining t-distributed neighbor retrieval visualizer (t-NeRV) and energy models. In the process of layout, our algorithm considers global and local structures at the same time. The layout results are more conform to aesthetic standards, meanwhile, maintain the structural information of the graph. We evaluate our algorithm on a wide variety of datasets and compare it with many other methods. We produce better visualization results than tsNET and tsNET* methods by reducing the tendency to crowd points together, and can better capture the global structure of the graph.

Keywords

• Graph layout
• dimension reduction
• energy model
• t-NeRV