# Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs

arXiv: https://arxiv.org/abs/2512.18673 · HTML: https://mlsanity.com/paper/2512.18673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2510.13368/) (2025)
- [Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems](https://mlsanity.com/paper/2508.20508/) (2025)
- [Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Models](https://mlsanity.com/paper/2511.01149/) (2025)
- [Structural Generalization for Microservice Routing Using Graph Neural Networks](https://mlsanity.com/paper/2510.15210/) (2025)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Structure-Aware Label Smoothing for Graph Neural Networks](https://mlsanity.com/paper/2112.00499/) (2021)
- [Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environments](https://mlsanity.com/paper/2512.13060/) (2025)
- [SHGNN: Structure-Aware Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2112.06244/) (2021)
- [Graph Neural Networks for User Satisfaction Classification in Human-Computer Interaction](https://mlsanity.com/paper/2511.04166/) (2025)

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

- [Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling](https://mlsanity.com/paper/2606.15640/) (2026)

## Abstract (arXiv)

This paper proposes a structure-aware driven scheduling graph modeling method to improve the accuracy and representation capability of anomaly identification in scheduling behaviors of complex systems. The method first designs a structure-guided scheduling graph construction mechanism that integrates task execution stages, resource node states, and scheduling path information to build dynamically evolving scheduling behavior graphs, enhancing the model's ability to capture global scheduling relationships. On this basis, a multi-scale graph semantic aggregation module is introduced to achieve semantic consistency modeling of scheduling features through local adjacency semantic integration and global topology alignment, thereby strengthening the model's capability to capture abnormal features in complex scenarios such as multi-task concurrency, resource competition, and stage transitions. Experiments are conducted on a real scheduling dataset with multiple scheduling disturbance paths set to simulate different types of anomalies, including structural shifts, resource changes, and task delays. The proposed model demonstrates significant performance advantages across multiple metrics, showing a sensitive response to structural disturbances and semantic shifts. Further visualization analysis reveals that, under the combined effect of structure guidance and semantic aggregation, the scheduling behavior graph exhibits stronger anomaly separability and pattern representation, validating the effectiveness and adaptability of the method in scheduling anomaly detection tasks.