ArticlePloS one2014
Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Connectivity of the adult human brain with sequential neurogenesis of circuits and transcriptomics signatures.Nature communications · 2025Article
- Diverging network architecture of theArXiv · 2025Article
- On collective behavior inFrontiers in neurorobotics · 2025Review
- Hierarchical communities in the larvalProceedings of the National Academy of Sciences of the United States of America · 2024Article
- A generative model of the connectome with dynamic axon growth.bioRxiv : the preprint server for biology · 2024Article
- A generative model of the connectome with dynamic axon growth.Network neuroscience (Cambridge, Mass.) · 2024Article
- Communities in C. elegans connectome through the prism of non-backtracking walks.Scientific reports · 2023Article
- The neuropeptidergic connectome of C. elegans.Neuron · 2023Article
- Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit.Current biology : CB · 2023Article
- The synaptic organization in theProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Circuit analysis of theNetwork neuroscience (Cambridge, Mass.) · 2023Article
- The brainstem connectome database.Scientific data · 2022Article
- Investigating the spectral features of the brain meso-scale structure at rest.Human brain mapping · 2021Article
- Neuronal classification from network connectivity via adjacency spectral embedding.Network neuroscience (Cambridge, Mass.) · 2021Article
- Bayesian modeling of dependence in brain connectivity data.Biostatistics (Oxford, England) · 2020Article
- Unifying the Notions of Modularity and Core-Periphery Structure in Functional Brain Networks during Youth.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.Network neuroscience (Cambridge, Mass.) · 2020Article
- Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling.Brain structure & function · 2020Article
- Article
- Weighted Stochastic Block Models of the Human Connectome across the Life Span.Scientific reports · 2018Article
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5 authors.
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Abstract
Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4-5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the "core-in-modules" decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems.
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