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Network dependence testing via diffusion maps and distance-based correlations

Author

Listed:
  • Youjin Lee
  • Cencheng Shen
  • Carey E Priebe
  • Joshua T Vogelstein

Abstract

SummaryDeciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.

Suggested Citation

  • Youjin Lee & Cencheng Shen & Carey E Priebe & Joshua T Vogelstein, 2019. "Network dependence testing via diffusion maps and distance-based correlations," Biometrika, Biometrika Trust, vol. 106(4), pages 857-873.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:4:p:857-873.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz045
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    Cited by:

    1. Cencheng Shen & Joshua T. Vogelstein, 2021. "The exact equivalence of distance and kernel methods in hypothesis testing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 385-403, September.

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