Fast Graph Laplacian Estimation using Effective Resistance
Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constrained graph estimation for Gaussian Markov random fields, focusing on the underdetermined regime in which the number of samples is smaller than the number of graph nodes. Existing approaches often formulate the problem as a sparsity-regularized maximum-likelihood estimation problem. While effective, such methods typically require iterative optimization and are often computationally demanding, particularly under Laplacian constraints. Instead, we propose a non-iterative estimator of graph Laplacians that uses effective resistance for regulariza...
| Authors | Christoffer Kjellson, Claudio Altafini, Emma Tegling |
|---|---|
| Primary category | eess.SP |
| Published | 20 September 2026 |
| Last updated at source | 30 September 2026 |
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