EC-SBM Synthetic Network Generator

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.

References in corpus (16)

• Finding and evaluating community structure in networks
• From Louvain to Leiden: guaranteeing well-connected communities
• Maps of random walks on complex networks reveal community structure
• Benchmark graphs for testing community detection algorithms
• Comparing community structure identification
• A Comparative Analysis of Community Detection Algorithms on Artificial Networks
• Community Discovery in Dynamic Networks: a Survey
• Community detection in networks: Structural communities versus ground truth
• Comparative Evaluation of Community Detection Algorithms: A Topological Approach
• Statistical significance of communities in networks
• Bayesian stochastic blockmodeling
• A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
• Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
• Towards realistic artificial benchmark for community detection algorithms evaluation
• On Accuracy of Community Structure Discovery Algorithms
• A generalized hypothesis test for community structure in networks