Abstract
Many networks exhibit small-world properties. The structure of a small-world network is characterized by short average path lengths and high clustering coefficients. Few graph layout methods capture this structure well which limits their effectiveness and the utility of the visualization itself. Here we present an extension to our novel graphTPP layout method for laying out small-world networks using only their topological properties rather than their node attributes. The Watts–Strogatz model is used to generate a variety of graphs with a small-world network structure. Community detection algorithms are used to generate six different clusterings of the data. These clusterings, the adjacency matrix and edgelist are loaded into graphTPP and, through user interaction combined with linear projections of the adjacency matrix, graphTPP is able to produce a layout which visually separates these clusters. These layouts are compared to the layouts of two force-based techniques. graphTPP is able to clearly separate each of the communities into a spatially distinct area and the edge relationships between the clusters show the strength of their relationship. As a secondary contribution, an edge-grouping algorithm for graphTPP is demonstrated as a means to reduce visual clutter in the layout and reinforce the display of the strength of the relationship between two communities.
Highlights
Small-world networks are a commonly occurring graph structure characterized by short average path lengths and high clustering coefficients [1]
Small-world networks are characterized by short average path lengths and high clustering coefficients
The Watts–Strogatz model aims to generate a graph with a high clustering coefficient and a short average path length, simulating the characteristics of a small-world network
Summary
Small-world networks are a commonly occurring graph structure characterized by short average path lengths and high clustering coefficients [1] This means that even when the network is large there are very few steps between each pair of nodes. It introduces an edgegrouping technique for reducing visual clutter caused by the edges in the graphTPP layout. The main contribution is the demonstration that graphTPP can be a viable layout method even when there is no typical node-attribute data available upon which to base the layout
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