the hive plot is a perceptually uniform and scalable linear layout visualization for network visual analytics
UNDERSTANDING NETWORK STRUCTURE WITH HIVE PLOTS.
(A) Normalized (top) and absolute (bottom) connectivity of E. coli gene regulatory network and Linux function call network (Yan et al.)
(B) Gene co-regulation networks in neuroblastoma samples.
(C) Network edges shown as ribbons creating circularly composited stacked bar plots (a periodic steamgraph).
(D) Syntenic network of three modern crucifer species to ancestral genome.
(E) Layered network correlation matrix. In each cell two layers u,v are depicted with u used to order axes and nodes while links for v are shown.
Hive plots — for the impatient
The hive plot is a rational visualization method for drawing networks. Nodes are mapped to and positioned on radially distributed linear axes — this mapping is based on network structural properties. Edges are drawn as curved links. Simple and interpretable.
The purpose of the hive plot is to establish a new baseline for visualization of large networks — a method that is both general and tunable and useful as a starting point in visually exploring network structure.
Hive plots give the reader a passing chance to quantitatively understand important aspects of a network's structure. Unlike hairballs, hive plots are excellent at managing the visual complexity arising from large number of edges and exposing both trends and outlier patterns in network structure.
only for networks?
Hive plots can be applied to data structures other than networks. The method requires that your data be mappable onto a set of pairwise relationships. For networks, this pairwise relationship is the edge between two nodes. In other circumstances, it can relate two spatial positions (where the axis corresponds to an object with a physical length scale) or two intervals (two axis segments are related, thereby creating a ratio comparison).
This hive plot provides a visual recipe for assessing the quality of a genomic assembly. An assembly is composed of reads (bottom axis), which are assembled into contigs (right axis). Independently, a reference assembly (left axis) may exist and act as a comparator. Among others, this hive plot answers the following questions
what fraction of reads are unassembled? 20%
what fraction of reads are unaligned to reference? 30%
what fraction of reference has no read coverage? 2%
what fraction of reference has no contig coverage? 15%
what fraction of reference is constructed by contigs < 200kb? 60%
are there contigs > 200kb? no.
what fraction of contigs are unaligned to the reference? 20%
what fraction of the overall assembly is derived from k=27 assembly? 80%
The benefit of this stacked bar plot layout is that the circular layout is both periodic and has visual weight. This approach is similar to a parallel coordinate plot, except here the plot wraps around.
Multiple panels can be combined to display a very large number of ratios. Below are shown 3 x 3 x 3 (27) comparisons, each with 3 x 8 ratios, for a total of 648 ratios. By categorizing each ratio using a spectral color scheme, patterns can be quickly spotted and interpreted. The image below was created for the EMBO Journal 2011 cover contest.