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Abstract Non‐perennial streams are receiving increased attention from researchers, however, suitable methods for measuring their hydrologic connectivity remain scarce. To address this deficiency, we developed Bayesian statistical approaches for measuring both average active stream length, and a new metric called average communication distance. Average communication distance is a theoretical increasedeffective distancethat stream‐borne materials must travel, given non‐continuous streamflow. Because it is the product of the inverse probability of surface water presence and stream length, the average communication distance of a non‐perennial stream segment will be greater than its actual physical length. As an application we considered Murphy Creek, a simple non‐perennial stream network in southwestern Idaho, USA. We used surface water presence/absence data obtained in 2019, and priors for the probability of surface water, based on predictions from an existing regional United States Geological Survey model. Average communication distance posterior distributions revealed locations where effective stream lengths increased dramatically due to flow rarity. We also found strong seasonal (spring, summer, fall) differences in network‐level posterior distributions of both average stream length and average communication distance. Our work demonstrates the unique perspectives concerning network drying provided by communication distance, and demonstrates the general usefulness of Bayesian approaches in the analysis of non‐perennial streams.
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The Gromov–Wasserstein distance between networks and stable network invariants
Abstract We define a metric—the network Gromov–Wasserstein distance—on weighted, directed networks that is sensitive to the presence of outliers. In addition to proving its theoretical properties, we supply network invariants based on optimal transport that approximate this distance by means of lower bounds. We test these methods on a range of simulated network datasets and on a dataset of real-world global bilateral migration. For our simulations, we define a network generative model based on the stochastic block model. This may be of independent interest for benchmarking purposes.
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- PAR ID:
- 10125348
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Information and Inference: A Journal of the IMA
- Volume:
- 8
- Issue:
- 4
- ISSN:
- 2049-8764
- Page Range / eLocation ID:
- p. 757-787
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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