Abstract
Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node’s position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.
Highlights
Networks offer an intuitive visual representation of complex systems
(4) the big size of many real-world networks is a key limiting factor for producing comprehensible layouts, leading to proverbial hair-ball visualizations. In this Brief Communication we introduce a framework for generating network layouts that address these challenges by using dimensionality reduction to directly encode network properties into node positions
All nodes except for those in the outermost level have the same number of neighbors, and all nodes within the same level have identical centrality values
Summary
Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. We first compile a set of F features for each of N nodes, incorporating any structural or functional characteristic we wish to be visually reflected in the final layout.
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