EC-SBM Synthetic Network Generator

TL;DR

EC-SBM is a new synthetic network generator that accurately replicates real-world community structures and connectivity, enabling better evaluation of community detection algorithms at scale.

Contribution

We introduce EC-SBM, a scalable synthetic network generator that closely mimics real-world clustered networks, improving upon existing methods in accuracy and efficiency.

Findings

EC-SBM achieves high accuracy in replicating network and community structures.

EC-SBM outperforms current alternative approaches in accuracy.

EC-SBM scales efficiently to networks with millions of nodes.

Abstract

Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our extensive performance study on large real-world networks shows that EC-SBM has high accuracy in both network and community-specific criteria, and is generally more accurate than current alternative approaches for this problem. Furthermore, EC-SBM is fast enough to scale to real-world networks with millions of nodes.