Compression of Biological Networks using a Genetic Algorithm with Localized Merge

Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.