# Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?

arXiv: https://arxiv.org/abs/2501.02766 · HTML: https://mlsanity.com/paper/2501.02766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Graph Neural Networks with Learnable Structural and Positional Representations](https://mlsanity.com/paper/2110.07875/) (2021)
- [CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms](https://mlsanity.com/paper/2111.03753/) (2021)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data](https://mlsanity.com/paper/2302.05092/) (2023)
- [Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis](https://mlsanity.com/paper/2407.01710/) (2024)
- [Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System](https://mlsanity.com/paper/2310.04701/) (2023)
- [TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework with Multimodal Data](https://mlsanity.com/paper/2407.19711/) (2024)

## Built on by (most cited first, 1 of 1)

- [Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring](https://mlsanity.com/paper/2609.27069/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) are widely adopted for fault diagnosis in microservice systems, premised on their ability to model service dependencies. However, the necessity of explicit graph structures remains underexamined, as existing evaluations conflate preprocessing with architectural contributions. To isolate the true value of GNNs, we propose DiagMLP, a deliberately minimal, topology-agnostic baseline that retains multimodal fusion capabilities while excluding graph modeling. Through ablation experiments across five datasets, DiagMLP achieves performance parity with state-of-the-art GNN-based methods in fault detection, localization, and classification. These findings challenge the prevailing assumption that graph structures are indispensable, revealing that: (i) preprocessing pipelines already encode critical dependency information, and (ii) GNN modules contribute marginally beyond multimodality fusion. Our work advocates for systematic re-evaluation of architectural complexity and highlights the need for standardized baseline protocols to validate model innovations.