# Novel Algorithm for Computing All-Pairs Homogeneity-Arc Binary-State Undirected Network Reliability

arXiv: https://arxiv.org/abs/2105.01500 · HTML: https://mlsanity.com/paper/2105.01500/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2021.

## Built on by (most cited first, 3 of 3)

- [Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems](https://mlsanity.com/paper/2202.07837/) (2022)
- [Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability](https://mlsanity.com/paper/2409.15721/) (2024)
- [Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems](https://mlsanity.com/paper/2503.15545/) (2025)

## Abstract (arXiv)

Among various real-life emerging applications, wireless sensor networks, Internet of Things, smart grids, social networks, communication networks, transportation networks, and computer grid systems, etc., the binary-state network is the fundamental network structure and model with either working or failed binary components. The network reliability is an effective index for assessing the network function and performance. Hence, the network reliability between two specific nodes has been widely adopted and more efficient network reliability algorithm is always needed. To have complete information for a better decision, all-pairs network reliability thus arises correspondingly. In this study, a new algorithm called the all-pairs BAT is proposed by revising the binary-addition-tree algorithm (BAT) and the layered-search algorithm (LSA). From both the theoretical analysis and the practical experiments conducted on 20 benchmark problems, the proposed all-pairs BAT is more efficient than these algorithms by trying all combinations of any pairs of nodes.