Versatility of nodal affiliation to communities

@article{Shinn2017VersatilityON,
  title={Versatility of nodal affiliation to communities},
  author={Maxwell Shinn and Rafael Romero-Garc{\'i}a and Jakob Seidlitz and Franti{\vs}ek V{\'a}{\vs}a and Petra E. V{\'e}rtes and Edward T. Bullmore},
  journal={Scientific Reports},
  year={2017},
  volume={7}
}
Graph theoretical analysis of the community structure of networks attempts to identify the communities (or modules) to which each node affiliates. However, this is in most cases an ill-posed problem, as the affiliation of a node to a single community is often ambiguous. Previous solutions have attempted to identify all of the communities to which each node affiliates. Instead of taking this approach, we introduce versatility, V, as a novel metric of nodal affiliation: V ≈ 0 means that a node is… 
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24 Citations

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It is demonstrated that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap.

Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition

The reliability and validity of one assignment method, the mixed membership algorithm, is investigated and its potential utility for identifying gaps in existing network models of cognition is explored.

Structural brain network of gifted children has a more integrated and versatile topology

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A counter-intuitive role played by the modular architecture of the brain in promoting global interaction is revealed by considering a process of diffusive propagation and demonstrating that this architecture, instead of localizing the activity, facilitates rapid communication across the connectome.

Graph theory methods: applications in brain networks

  • O. Sporns
  • Computer Science
    Dialogues in clinical neuroscience
  • 2018
A brief review surveys some of the most commonly used and neurobiologically insightful graph measures and techniques, including the detection of network communities or modules, and the identification of central network elements that facilitate communication and signal transfer.

Characterising disease-related and developmental changes in correlation-derived structural and functional brain networks

Applying probabilistic thresholding eliminates increased network randomisation in schizophrenia, supporting the hypothesis that previously reported group differences originated in the application of standard thresholding approaches to patient networks with decreased functional correlations.

References

SHOWING 1-10 OF 35 REFERENCES

Finding community structure in networks using the eigenvectors of matrices.

  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2006
A modularity matrix plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations, and a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong are proposed.

Finding and evaluating community structure in networks.

  • M. NewmanM. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Consensus clustering in complex networks

It is shown that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions.

Community detection in networks: Modularity optimization and maximum likelihood are equivalent

An exact equivalence is demonstrated between two widely used methods of community detection in networks which incorporates a resolution parameter controlling the size of the communities discovered, and the method of maximum likelihood applied to the special case of the stochastic block model known as the planted partition model.

Robust Detection of Dynamic Community Structure in Networks

This work considers the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems and develops a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions.

Higher-order organization of complex networks

A generalized framework for clustering networks on the basis of higher-order connectivity patterns provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges.

Equivalence between modularity optimization and maximum likelihood methods for community detection.

An exact equivalence is shown between maximization of the generalized modularity that includes a resolution parameter and the special case of the block model known as the planted partition model, in which all communities in a network are assumed to have statistically similar properties.

Statistical mechanics of community detection.

The properties of the ground state configuration are elucidated to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study.

Motif-based communities in complex networks

It is shown how motifs can be used to define general classes of nodes, including communities, by extending the mathematical expression of Newman–Girvan modularity by constructing a general framework and applying it to some synthetic and real networks.

Fast unfolding of communities in large networks

This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.