A scalable null model for directed graphs matching all degree distributions: In, out, and reciprocal

@article{Durak2013ASN,
  title={A scalable null model for directed graphs matching all degree distributions: In, out, and reciprocal},
  author={N. Durak and T. Kolda and Ali Pinar and Seshadhri Comandur},
  journal={2013 IEEE 2nd Network Science Workshop (NSW)},
  year={2013},
  pages={23-30}
}
  • N. Durak, T. Kolda, +1 author Seshadhri Comandur
  • Published 2013
  • Mathematics, Computer Science, Physics
  • 2013 IEEE 2nd Network Science Workshop (NSW)
  • Degree distributions are arguably the most important property of real world networks. The classic edge configuration model or Chung-Lu model can generate an undirected graph with any desired degree distribution. This serves as a good null model to compare algorithms or perform experimental studies. Furthermore, there are scalable algorithms that implement these models and they are invaluable in the study of graphs. However, networks in the real-world are often directed, and have a significant… CONTINUE READING
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