# RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

arXiv: https://arxiv.org/abs/2201.12263 · HTML: https://mlsanity.com/paper/2201.12263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Graph Neural Networks: Architectures, Stability and Transferability](https://mlsanity.com/paper/2008.01767/) (2020)
- [A Practical Tutorial on Graph Neural Networks](https://mlsanity.com/paper/2010.05234/) (2020)
- [Message-Passing Neural Networks Learn Little's Law](https://mlsanity.com/paper/1901.05748/) (2019)
- [Graphical Models for Zero-Inflated Single Cell Gene Expression](https://mlsanity.com/paper/1610.05857/) (2016)
- [Joint Reliability-aware and Cost Efficient Path Allocation and VNF Placement using Sharing Scheme](https://mlsanity.com/paper/1912.06742/) (2019)
- [Reliability Modeling and Analysis of Communication Networks](https://mlsanity.com/paper/1612.08910/) (2016)

## Abstract (arXiv)

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab'asi-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement.