Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs, which are typically depicted on the graph as node colour, shape or size. Here we demonstrate that these attributes can instead be used to layout the graph in highdimensional data space. Then using a dimension reduction technique, targeted

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... ction pursuit, the graph layout can be optimised for displaying clustering. The technique outperforms force-directed layout methods in cluster separation when applied to a sample, artificially generated, small-world network.