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Publication Detail
Subsampling large graphs and invariance in networks
  • Publication Type:
    Journal article
  • Authors:
    Orbanz P
  • Publication date:
  • Keywords:
    math.ST, math.ST, math.PR, stat.TH
Specify a randomized algorithm that, given a very large graph or network, extracts a random subgraph. What can we learn about the input graph from a single subsample? We derive laws of large numbers for the sampler output, by relating randomized subsampling to distributional invariance: Assuming an invariance holds is tantamount to assuming the sample has been generated by a specific algorithm. That in turn yields a notion of ergodicity. Sampling algorithms induce model classes---graphon models, sparse generalizations of exchangeable graphs, and random multigraphs with exchangeable edges can all be obtained in this manner, and we specialize our results to a number of examples. One class of sampling algorithms emerges as special: Roughly speaking, those defined as limits of random transformations drawn uniformly from certain sequences of groups. Some known pathologies of network models based on graphons are explained as a form of selection bias.
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