# An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks

arXiv: https://arxiv.org/abs/2105.03092 · HTML: https://mlsanity.com/paper/2105.03092/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 3 of 3)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Causal Discovery with Continuous Additive Noise Models](https://mlsanity.com/paper/1309.6779/) (2013)

## Built on by (most cited first, 10 of 10)

- [gCastle: A Python Toolbox for Causal Discovery](https://mlsanity.com/paper/2111.15155/) (2021)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [PyRCA: A Library for Metric-based Root Cause Analysis](https://mlsanity.com/paper/2306.11417/) (2023)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks](https://mlsanity.com/paper/2507.18190/) (2025)
- [Inference of Causal Networks using a Topological Threshold](https://mlsanity.com/paper/2404.14460/) (2024)
- [TempOpt -- Unsupervised Alarm Relation Learning for Telecommunication Networks](https://mlsanity.com/paper/2508.08814/) (2025)
- [Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models](https://mlsanity.com/paper/2508.12500/) (2025)
- [Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure](https://mlsanity.com/paper/2601.07342/) (2026)
- [EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis](https://mlsanity.com/paper/2607.27290/) (2026)

## Abstract (arXiv)

Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.