ArticleScientific reports2019
Reduced network extremal ensemble learning (RenEEL) scheme for community detection in complex networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Oviposition Inhibitory Neuron is a Potential Hub of Multi-Circuit Integration in theeNeuro · 2025Article
- Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL).Entropy (Basel, Switzerland) · 2025Article
- Hierarchical Modular Structure of theThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data.Frontiers in genetics · 2021Article
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Authors and funding
3 authors.
Funding
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Abstract
We introduce an ensemble learning scheme for community detection in complex networks. The scheme uses a Machine Learning algorithmic paradigm we call Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble of network partitions, which can be found by a conventional base algorithm, to find a node partition that maximizes modularity. At each iteration, core groups of nodes that are in the same community in every ensemble partition are identified and used to form a reduced network. Partitions of the reduced network are then found and used to update the ensemble. The smaller size of the reduced network makes the scheme efficient. We use the scheme to analyze the community structure in a set of commonly studied benchmark networks and find that it outperforms all other known methods for finding the partition with maximum modularity.
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