Versatility of nodal affiliation to communities

  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},
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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  • 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.

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  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2006
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  • 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.

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Statistical mechanics of community detection.

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Motif-based communities in complex networks

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Fast unfolding of communities in large networks

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Functional cartography of complex metabolic networks

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