RECCS: REALISTIC CLUSTER CONNECTIVITY SIMULATOR FOR SYNTHETIC NETWORK GENERATION

Lahari Anne (), The-Anh Vu-Le (), Minhyuk Park (), Tandy Warnow () and George Chacko
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Lahari Anne: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
The-Anh Vu-Le: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
Minhyuk Park: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
Tandy Warnow: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
George Chacko: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA

Advances in Complex Systems (ACS), 2025, vol. 28, issue 05, 1-21

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 comparable 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.

Keywords: Synthetic networks; community detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219525925400041

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