Simple Graph Comparison Inspired on Metabolic Pathway Correlation

Abstract

Comparing two graphs is a computationally difficult task [9], [8]. After a work by E. Arias-Mendez and F. Torres-Rojas [7] about the correlation of metabolic pathways with two new proposed approaches to simplify the comparison of its associated graph representation, we extended this work to general graph structures as a simple way to compare them. The approach presented here is an extension of those algorithms to general graphs. The first algorithm proposed looks to transform the comparing graphs into linear sequences, to be analyzed using sequence-alignment tools from bioinformatics and get a numeric score as its value of similitude. The second proposed algorithm consists of the search of equal connected nodes between 2 graphs to eliminate then on both structures, only leaving the differences, as heuristic for comparison. These algorithms were developed as a low-cost process to correlate metabolic pathways showing good results; the suggestion is to use this information as a previous analysis to a deeper, more expensive, comparing tools use. Here we review the extension of this work as an application to a more general graph data structure. These methods have shown to be an effective way to treat the problem as listed in the results section.