Technology Category
Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityMachine Learning: Graph-based Machine LearningSearch and Optimization: Sampling/Simulation-based Search
EC-SBM Synthetic Network Generator
🤖 AI Summary
Synthetic networks often fail to simultaneously preserve both global topology and intra-community structural fidelity, thereby limiting the accuracy of community detection algorithm evaluation.
Method: This paper proposes the EC-SBM model—the first to explicitly model and reproduce empirically observed edge connection patterns within real communities—overcoming the limitation of conventional Stochastic Block Models (SBMs) that only control inter-block edge probabilities. EC-SBM integrates empirically derived community partition constraints, local density estimation, and an efficient sampling mechanism to achieve dual-granularity fidelity at both network- and community-levels.
Results: Evaluated on million-node real-world networks, EC-SBM reduces global statistical metric error by 37% and improves intra-community edge connectivity matching accuracy by 52% over state-of-the-art methods, demonstrating both high fidelity and scalability.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Generate realistic synthetic networks
Improve community detection evaluation
Simulate internal cluster connectivity
Innovation
Methods, ideas, or system contributions that make the work stand out.
Edge-Connected Stochastic Block Model
Simulates internal cluster connectivity
Scales to millions of nodes
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