# Contrastive Learning for Correlating Network Incidents

arXiv: https://arxiv.org/abs/2509.24446 · HTML: https://mlsanity.com/paper/2509.24446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [NeuralWarp: Time-Series Similarity with Warping Networks](https://mlsanity.com/paper/1812.08306/) (2018)
- [Graph-based Incident Aggregation for Large-Scale Online Service Systems](https://mlsanity.com/paper/2108.12179/) (2021)
- [GraphWeaver: Billion-Scale Cybersecurity Incident Correlation](https://mlsanity.com/paper/2406.01842/) (2024)
- [Alert Correlation Algorithms: A Survey and Taxonomy](https://mlsanity.com/paper/1811.00921/) (2018)

## Abstract (arXiv)

Internet service providers monitor their networks to detect, triage, and remediate service impairments. When an incident is detected, it is important to determine whether similar incidents have occurred in the past or are happening concurrently elsewhere in the network. Manual correlation of such incidents is infeasible due to the scale of the networks under observation, making automated correlation a necessity. This paper presents a self-supervised learning method for similarity-based correlation of network situations. Using this method, a deep neural network is trained on a large unlabeled dataset of network situations using contrastive learning. High precision achieved in experiments on real-world network monitoring data suggests that contrastive learning is a promising approach to network incident correlation.