Uncovering fuzzy community structure in complex networks.

@article{Zhang2007UncoveringFC,
  title={Uncovering fuzzy community structure in complex networks.},
  author={Shihua Zhang and Rui-Sheng Wang and Xiang-Sun Zhang},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2007},
  volume={76 4 Pt 2},
  pages={
          046103
        }
}
There has been an increasing interest in properties of complex networks, such as small-world property, power-law degree distribution, and network transitivity which seem to be common to many real world networks. In this study, a useful community detection method based on non-negative matrix factorization (NMF) technique is presented. Based on a popular modular function, a proper feature matrix from diffusion kernel and NMF algorithm, the presented method can detect an appropriate number of… 
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References

SHOWING 1-10 OF 56 REFERENCES

Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis

A hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks based on the diffusion kernels of networks and it is found that it can detect significant community structure in these networks.

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
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.

Detecting fuzzy community structures in complex networks with a Potts model.

A fast community detection algorithm based on a q-state Potts model that allows for the detection of overlapping ("fuzzy") communities and quantifying the association of nodes with multiple communities as well as the robustness of a community.

Method to find community structures based on information centrality.

An algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality is developed that is very effective especially when the communities are very mixed and hardly detectable by the other methods.

Distance, dissimilarity index, and network community structure.

  • Haijun Zhou
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2003
This work calculates the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized, and identifies many clusters that have well defined biological functions.

Detecting community structure in networks

A number of more recent algorithms that appear to work well with real-world network data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks are described.

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.

Defining and identifying communities in networks.

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.

Identification of overlapping community structure in complex networks using fuzzy c-means clustering

Finding instabilities in the community structure of complex networks.

A method to identify the nodes lying "between clusters," allowing for a general measure of the stability of the clusters, is introduced by adding noise over the edge weights.
...