RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation

2025-02-04

AI Summary

Synthetic networks generated by the Stochastic Block Model (SBM) frequently yield disconnected ground-truth communities, severely undermining their reliability for evaluating community detection algorithms. Method: We propose RECCS—a framework that first generates an SBM network and then applies a structured edge rewiring procedure to enforce internal connectivity of all ground-truth communities while strictly preserving key topological properties, including the degree distribution and modularity. Contribution/Results: We systematically identify, for the first time, the intrinsic disconnectivity flaw of SBM even under standard community parameters, and design a provably quality-preserving connectivity repair algorithm. Extensive experiments—parameterized by real-world networks up to 13.9 million nodes—demonstrate that RECCS raises community connectivity from 72% to 99.8% and reduces modularity deviation by over 40%, substantially enhancing structural fidelity of synthetic networks to real-world community organization.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityReasoning under Uncertainty: Relational Probabilistic ModelsSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsSocial Networks and Social Media: Computational social scienceWeb Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• Improve cluster connectivity in synthetic networks
• Ensure ground-truth clusters remain connected
• Enhance SBM for realistic network generation

Innovation

• Ensures connected ground-truth clusters
• Modifies SBM for improved connectivity
• Handles networks up to 13.9 million nodes