Fast unfolding of communities in large networks

@article{Blondel2008FastUO,
  title={Fast unfolding of communities in large networks},
  author={Vincent D. Blondel and Jean-Loup Guillaume and Renaud Lambiotte and Etienne Lefebvre},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  year={2008},
  volume={2008},
  pages={P10008}
}
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web… 

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References

SHOWING 1-10 OF 43 REFERENCES

Ju l 2 00 8 Fast unfolding of communities in large networks

TLDR
The method is a heuristic method that is based on modularity optimization that is shown to outperform all other known community detection method in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.

Finding and evaluating community structure in networks.

  • M. NewmanM. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
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.

Community detection in complex networks using extremal optimization.

  • J. DuchA. Arenas
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2005
TLDR
The method outperforms the optimal modularity found by the existing algorithms in the literature and is feasible to be used for the accurate identification of community structure in large complex networks.

Fast algorithm for detecting community structure in networks.

  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.

Near linear time algorithm to detect community structures in large-scale networks.

TLDR
This paper investigates a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities.

Finding community structure in networks using the eigenvectors of matrices.

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

Defining and identifying communities in networks.

TLDR
This article proposes a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability and applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods.

Finding community structure in very large networks.

TLDR
A hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure.

Community structure in social and biological networks

  • M. GirvanM. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

Modularity and community structure in networks.

  • M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2006
TLDR
It is shown that the modularity of a network can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which is called modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times.