Corpus ID: 32534613

Detecting Topological Changes in Dynamic Community Networks

@article{Wills2017DetectingTC,
  title={Detecting Topological Changes in Dynamic Community Networks},
  author={Peter Wills and François G. Meyer},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.07362}
}
  • Peter Wills, François G. Meyer
  • Published in ArXiv 2017
  • Computer Science, Physics
  • The study of time-varying (dynamic) networks (graphs) is of fundamental importance for computer network analytics. Several methods have been proposed to detect the effect of significant structural changes in a time series of graphs. The main contribution of this work is a detailed analysis of a dynamic community graph model. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. It is a dynamic extension of the well-known stochastic blockmodel. The goal… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 59 REFERENCES

    Modern Graph Theory

    • Béla Bollobás
    • Mathematics, Computer Science
    • Graduate Texts in Mathematics
    • 2002
    VIEW 12 EXCERPTS
    HIGHLY INFLUENTIAL

    The Resistance Perturbation Distance: A Metric for the Analysis of Dynamic Networks

    VIEW 5 EXCERPTS

    Hitting and commute times in large random neighborhood graphs

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Incremental Computation of Pseudo-Inverse of Laplacian

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    A Survey on Social Media Anomaly Detection

    Exact Recovery in the Stochastic Block Model

    VIEW 3 EXCERPTS