Symmetry-driven network reconstruction through pseudobalanced coloring optimization

Abstract

Symmetries found through automorphisms or graph fibrations provide important insights in network analysis. Symmetries identify clusters of robust synchronization in the network which improves the understanding of the functionality of complex biological systems. Network symmetries can be determined by finding a balanced coloring of the graph, which is a node partition in which each cluster of nodes receives the same information (color) from the rest of the graph.Networks based on real systems, however, are built on experimental data which are inherently incomplete, due to missing links, collection errors, and natural variations within specimens of the same biological species. Therefore, a method to find pseudosymmetries and repair networks based on those symmetries is important when analyzing real world networks. In this paper we introduce the pseudobalanced coloring (PBCIP) problem, and provide an integer programming formulation which (a) calculates a pseudobalanced coloring of the graph taking into account the missing data, and (b) optimally repairs the graph with the minimal number of added/removed edges to maximize the symmetry of the graph. We apply our formulation to the C. elegans connectome to find pseudocoloring and the optimal graph repair. Our solution compares well with a manually curated ground-truth C. elegans graph as well as solutions generated by other methods of missing link prediction. Furthermore, we provide an extension of the algorithm using Bender's decomposition that allows our formulation to be applied to larger networks.