On the Phase Transition of Finding a Biclique in a larger Bipartite Graph

TL;DR

This paper investigates the phase transition phenomenon in finding complete bipartite subgraphs, revealing an easy-hard-easy-hard-easy pattern and identifying key features that distinguish hard instances using decision tree classifiers.

Contribution

It introduces a method to identify an order parameter for phase transition in bipartite subgraph problems and characterizes the pattern of instance difficulty across different sizes.

Findings

Hard instances are more likely to contain the complete bipartite subgraph.

Easy instances tend to have negligible computational cost.

The phase transition pattern is consistent across various problem sizes.

Abstract

We report on the phase transition of finding a complete subgraph, of specified dimensions, in a bipartite graph. Finding a complete subgraph in a bipartite graph is a problem that has growing attention in several domains, including bioinformatics, social network analysis and domain clustering. A key step for a successful phase transition study is identifying a suitable order parameter, when none is known. To this purpose, we have applied a decision tree classifier to real-world instances of this problem, in order to understand what problem features separate an instance that is hard to solve from those that is not. We have successfully identified one such order parameter and with it the phase transition of finding a complete bipartite subgraph of specified dimensions. Our phase transition study shows an easy-to-hard-to-easy-to-hard-to-easy pattern. Further, our results indicate that the hardest instances are in a region where it is more likely that the corresponding bipartite graph will have a complete subgraph of specified dimensions, a positive answer. By contrast, instances with a negative answer are more likely to appear in a region where the computational cost is negligible. This behaviour is remarkably similar for problems of a number of different sizes.