Structural Entropy of the Stochastic Block Models

TL;DR

This paper extends the concept of structural entropy from unlabeled graphs to stochastic block models with multiple parts, providing a way to efficiently compress such models while preserving their structural information.

Contribution

It introduces a partitioned structural entropy for stochastic block models and develops an asymptotically optimal compression scheme for them.

Findings

Computed the partitioned structural entropy for stochastic block models.

Developed a compression scheme that asymptotically achieves the entropy limit.

Generalized structural entropy to include partition information.

Abstract

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the "structural information" only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erd\H{o}s--R\'enyi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the Stochastic Block Models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for Stochastic Block Models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the Stochastic Block Models, and provide a compression scheme that asymptotically achieves this entropy limit.