Improved Community Detection using Stochastic Block Models
Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
19.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Community detection approaches resolve complex networks into smaller groups
(communities) that are expected to be relatively edge-dense and well-connected.
The stochastic block model (SBM) is one of several approaches used to uncover
community structure in graphs. In this study, we demonstrate that SBM software
applied to various real-world and synthetic networks produces poorly-connected
to disconnected clusters. We present simple modifications to improve the
connectivity of SBM clusters, and show that the modifications improve accuracy
using simulated networks. |
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DOI: | 10.48550/arxiv.2408.10464 |