Unfolding Ordered Matrices into BioFabric Motifs
This work provides a practical algorithm for automatically generating BioFabric layouts, which is useful for graph visualization researchers and practitioners, but the approach is incremental as it combines existing techniques.
The paper addresses the problem of automatically generating high-quality BioFabric visualizations of graphs by ordering vertices and edges to expose patterns. It presents a pipeline that uses Moran's I to order the adjacency matrix and detects patterns, then unfolds them into BioFabric motifs, handling graphs up to 250 vertices.
BioFabrics were introduced by Longabaugh in 2012 as a way to draw large graphs in a clear and uncluttered manner. The visual quality of BioFabrics crucially depends on the order of vertices and edges, which can be chosen independently. Effective orders can expose salient patterns, which in turn can be summarized by motifs, allowing users to take in complex networks at-a-glance. However, so far there is no efficient layout algorithm which automatically recognizes patterns and delivers both a vertex and an edge ordering that allows these patterns to be expressed as motifs. In this paper we show how to use well-ordered matrices as a tool to efficiently find good vertex and edge orders for BioFabrics. Specifically, we order the adjacency matrix of the input graph using Moran's $I$ and detect (noisy) patterns with our recent algorithm. In this note we show how to "unfold" the ordered matrix and its patterns into a high-quality BioFabric. Our pipelines easily handles graphs with up to 250 vertices.