Offdiagonal complexity: A computationally quick network complexity measure—application to protein networks and cell division

Abstract

Many complex biological, social, and economical networks show topologies drastically differing from random graphs. But what is a complex network, i.e., how can one quantify the complexity of a graph? Here the Offdiagonal Complexity (OdC), a new, and computationally cheap, measure of complexity is defined, based on the node-node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient, and average path length. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates thereof. In addition, OdC is used to characterize the spatial complexity of cell aggregates. We investigate the earliest embryo development states of Caenorhabditis elegans. The development states of the premorphogenetic phase are represented by symmetric binary-valued cell connection matrices with dimension growing from 4 to 385. These matrices can be interpreted as adjacency matrices of an undirected graph, or network. The OdC approach allows us to describe quantitatively the complexity of the cell aggregate geometry.