Structure induction by lossless graph compression

TL;DR

This paper introduces Graphitour, a novel lossless graph compression algorithm that automates structure discovery in complex relational data like genomic networks, extending grammatical inference techniques to graphs.

Contribution

The work presents a new graph compression algorithm, Graphitour, capable of inducing structure from various graph types, including directed and labeled graphs, for improved data representation.

Findings

Successfully applied to DNA nested structures

Extends grammatical inference to graphs

Handles diverse graph types

Abstract

This work is motivated by the necessity to automate the discovery of structure in vast and evergrowing collection of relational data commonly represented as graphs, for example genomic networks. A novel algorithm, dubbed Graphitour, for structure induction by lossless graph compression is presented and illustrated by a clear and broadly known case of nested structure in a DNA molecule. This work extends to graphs some well established approaches to grammatical inference previously applied only to strings. The bottom-up graph compression problem is related to the maximum cardinality (non-bipartite) maximum cardinality matching problem. The algorithm accepts a variety of graph types including directed graphs and graphs with labeled nodes and arcs. The resulting structure could be used for representation and classification of graphs.