# Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems

arXiv: https://arxiv.org/abs/2503.15545 · HTML: https://mlsanity.com/paper/2503.15545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Novel Binary-Addition Tree Algorithm (BAT) for Binary-State Network Reliability Problem](https://mlsanity.com/paper/2004.08238/) (2020)
- [Novel Algorithm for Computing All-Pairs Homogeneity-Arc Binary-State Undirected Network Reliability](https://mlsanity.com/paper/2105.01500/) (2021)
- [Novel Binary Addition Tree Algorithm (BAT) for Calculating the Direct Lower-Bound of the Highly Reliable Binary-State Network Reliability](https://mlsanity.com/paper/2110.13390/) (2021)
- [A New BAT and PageRank algorithm for Propagation Probability in Social Networks](https://mlsanity.com/paper/2202.13033/) (2022)
- [Novel Recursive Inclusion-Exclusion Technology Based on BAT and MPs for Heterogeneous-Arc Binary-State Network Reliability Problems](https://mlsanity.com/paper/2207.00169/) (2022)

## Abstract (arXiv)

Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between accuracy, scalability, and data efficiency. This study evaluates 20 machine learning methods across three reliability regimes full range (0.0-1.0), high reliability (0.9-1.0), and ultra high reliability (0.99-1.0) to address these gaps. We demonstrate that large-scale networks with arc reliability larger than or equal to 0.9 exhibit near-unity system reliability, enabling computational simplifications. Further, we establish a dataset-scale-driven paradigm for algorithm selection: Artificial Neural Networks (ANN) excel with limited data, while Polynomial Regression (PR) achieves superior accuracy in data-rich environments. Our findings reveal ANN's Test-MSE of 7.24E-05 at 30,000 samples and PR's optimal performance (5.61E-05) at 40,000 samples, outperforming traditional Monte Carlo simulations. These insights provide actionable guidelines for balancing accuracy, interpretability, and computational efficiency in reliability engineering, with implications for infrastructure resilience and system optimization.