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Creators/Authors contains: "Vu-Le, The-Anh"

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  1. ; ; ; (, Applied Network Science)
    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 performance study on large real-world networks shows that EC-SBM is generally more accurate with respect to network and community criteria than currently used approaches for this problem. Furthermore, we demonstrate that EC-SBM can complete analyses on several real-world networks with millions of nodes. 
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    Free, publicly-accessible full text available December 1, 2026
  2. ; ; ; ; (, Springer Nature Switzerland)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; ; ; ; (, Springer Nature Switzerland)
    Free, publicly-accessible full text available January 1, 2026