RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation

TL;DR

RECCS is a novel synthetic network generator that improves cluster connectivity in SBM-based models, enabling more realistic evaluation of community detection methods on large-scale networks.

Contribution

The paper introduces RECCS, a method that enhances SBM-generated networks by ensuring better cluster connectivity while maintaining other network properties.

Findings

RECCS produces networks with improved cluster connectivity.

RECCS scales to networks with up to 13.9 million nodes.

Enhanced synthetic networks better mimic real-world community structures.

Abstract

The limited availability of useful ground-truth communities in real-world networks presents a challenge to evaluating and selecting a "best" community detection method for a given network or family of networks. The use of synthetic networks with planted ground-truths is one way to address this challenge. While several synthetic network generators can be used for this purpose, Stochastic Block Models (SBMs), when provided input parameters from real-world networks and clusterings, are well suited to producing networks that retain the properties of the network they are intended to model. We report, however, that SBMs can produce disconnected ground truth clusters; even under conditions where the input clusters are connected. In this study, we describe the REalistic Cluster Connectivity Simulator (RECCS), which, while retaining approximately the same quality for other network and cluster parameters, creates an SBM synthetic network and then modifies it to ensure an improved fit to cluster connectivity. We report results using parameters obtained from clustered real-world networks ranging up to 13.9 million nodes in size, and demonstrate an improvement over the unmodified use of SBMs for network generation.