Enhancing community detection by using local structural information

@article{Xiang2016EnhancingCD,
  title={Enhancing community detection by using local structural information},
  author={Ju Xiang and Ke Hu and Yan Zhang and Meihua Bao and Liang Tang and Yan-Ni Tang and Yuan-Yuan Gao and Jianming Li and Benyan Chen and Jing-Bo Hu},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  year={2016},
  volume={2016}
}
  • Ju Xiang, K. Hu, Jing-Bo Hu
  • Published 4 January 2016
  • Computer Science
  • Journal of Statistical Mechanics: Theory and Experiment
Many real-world networks, such as gene networks, protein–protein interaction networks and metabolic networks, exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have a positive effect on community detection in the networks. Here, various local similarity measures are used to extract local structural information, which is… 

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References

SHOWING 1-10 OF 74 REFERENCES

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.

A class of improved algorithms for detecting communities in complex networks

Detecting network communities: a new systematic and efficient algorithm

An efficient and relatively fast algorithm for the detection of communities in complex networks is introduced. The method exploits spectral properties of the graph Laplacian matrix combined with

Overlapping community detection in networks with positive and negative links

TLDR
This paper proposes a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks and shows that the model outperforms other state-of-the-art models at shedding light on the community Detection in synthetic signed networks.

Finding Statistically Significant Communities in Networks

TLDR
OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

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 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.

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 overlapping communities in networks by label propagation

TLDR
The main contribution is to extend the label and propagation step to include information about more than one community: each vertex can now belong to up to v communities, where v is the parameter of the algorithm.
...