Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications

@inproceedings{Mungan2008StabilityOM,
  title={Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications},
  author={Muhittin Mungan and Jos{\'e} J. Ramasco},
  year={2008}
}
  • Muhittin Mungan, José J. Ramasco
  • Published 2008
  • Mathematics, Physics, Computer Science
  • Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 48 REFERENCES

    Simple rules yield complex food

    • RJ R.J. Williams, N N. Martinez
    • webs,
    • 2000
    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Artifacts or Attributes

    • N Martinez
    • Effects of Resolution on the Little Rock Lake food web,
    • 1991
    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Bayesian approach to network

    • JM Hofman, CH Wiggins
    • modularity, 2008 Phys. Rev. Lett
    • 2008
    VIEW 1 EXCERPT

    Bayesian approach to network modularity.

    Fast online graph clustering via Erdös-Rényi mixture

    VIEW 1 EXCERPT

    Mixed Membership Stochastic Blockmodels

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL