Detectability Limit of Graph Partitioning

Abstract

Graph partitioning as an inference problem has been an important topic in multiple fields of science. In this article, we derive a performance limit called the algorithmic detectability limit on graph partitioning using a technique developed in statistical physics. This limit is a phase transition point beyond which an algorithm completely loses the ability to identify the group structure that is assumed in a random graph model.