Differential Analysis of Directed Networks
1Department of Statistics, Purdue University, West Lafayette, IN.
Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence
|July 30, 2026
Summary
We created a new statistical method to find structural differences in networks using structural equation models. This approach improves network analysis by accurately identifying common and differential structures.
Area of Science:
- Statistics
- Network Analysis
- Econometrics
Background:
- Structural Equation Models (SEMs) are widely used for network analysis.
- Identifying differential network structures is crucial but challenging.
- Existing methods may struggle with accuracy and computational efficiency.
Purpose of the Study:
- To develop a novel statistical method for identifying structural differences in networks.
- To propose a reparameterization strategy to separate common and differential network structures.
- To design an algorithm for accurate estimation and selection of these structures.
Main Methods:
- A two-stage algorithm involving calibration and construction phases.
- Calibration stage: uses L2-regularized regression to correct for endogeneity and ensure consistent prediction.
- Construction stage: employs L1-regularized regression for selecting and estimating common and differential effects.
Main Results:
- Theoretical results establish non-asymptotic error bounds for predictions and estimates.
- Demonstrated consistency in identifying both common and differential network effects.
- Proposed method showed superior performance compared to independent network construction on synthetic data.
Conclusions:
- The novel statistical method effectively identifies structural differences in networks.
- The algorithm offers accurate predictions and estimates with theoretical guarantees.
- The method is computationally efficient, allowing for parallel processing and demonstrating practical applicability.
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