Learning multifractal structure in large networks

  title={Learning multifractal structure in large networks},
  author={Austin R. Benson and Carlos Riquelme and S. Schmit},
  journal={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
Using random graphs to model networks has a rich history. In this paper, we analyze and improve the multifractal network generators (MFNG) introduced by Palla et al. We provide a new result on the probability of subgraphs existing in graphs generated with MFNG. This allows us to quickly compute moments of an important set of graph properties, such as the expected number of edges, stars, and cliques for graphs generated using MFNG. Specifically, we show how to compute these moments in time… Expand
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