Sultan Amed, Sayantan Banerjee
arXiv 17 Sep 2026 · Statistics — Methodology
arXiv:2609.20044 · PDF · Extracted main text
Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observed turnover also reflects graph-selection error. We study the two-snapshot Hamming-turnover functional under a homogeneous edge-misclassification model. With known sensitivity and specificity and conditional independence of the estimated edge indicators across periods, latent turnover admits a closed-form unbiased adjustment based only on observed turnover and the two estimated graph sizes. We then examine the effects of sparsity, calibration error and dependence across periods. When the number of true links is proportional to $p$, a false-positive probability of order $p^{-1}$ generates expected spurious turnover of order $p$. An error of the same order in calibrating the false-positive probability can likewise leave order-$p$ bias after adjustment. We also derive the bias induced by cross-period dependence and give sufficient conditions for consistency relative to network size. Numerical results illustrate the finite-sample implications.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chang, J., Kolaczyk, E. D., and Yao, Q (2022) Estimation of subgraph densities in noisy networks | 0.644 | 2 | 2 | 100% |
| 2 | Balachandran, P., Kolaczyk, E. D., and Viles, W. D (2017) On the propagation of low-rate measurement error to subgraph counts in large networks | 0.405 | 1 | 1 | 100% |
| 3 | Bhachech, J., Chakrabarti, A., Kaizoji, T., and Chakrabarti, A. S (2022) Instability of networks: Effects of sampling frequency and extreme fluctuations in financial data | 0.405 | 1 | 1 | 100% |
| 4 | MacDonald, P. W. and Kolaczyk, E. D (2026) Inference for subgraph densities in noisy dynamic networks | 0.405 | 1 | 1 | 100% |
| 5 | Newman, M. E. J (2018) Estimating network structure from unreliable measurements | 0.405 | 1 | 1 | 100% |
| 6 | Zhao, S. D., Cai, T. T., and Li, H (2014) Direct estimation of differential networks | 0.405 | 1 | 1 | 100% |
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