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Anomaly Detection in Dynamic Graphs: A Comprehensive Survey

arXiv 2406.00134 ↗First posted 31 May 2024cs.LG cs.AIcited by 18

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Abstract (arXiv)

This survey paper presents a comprehensive and conceptual overview of anomaly detection using dynamic graphs. We focus on existing graph-based anomaly detection (AD) techniques and their applications to dynamic networks. The contributions of this survey paper include the following: i) a comparative study of existing surveys on anomaly detection; ii) a Dynamic Graph-based Anomaly Detection (DGAD) review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine-learning models, matrix transformations, probabilistic approaches, and deep-learning approaches; iii) a discussion of graphically representing both discrete and dynamic networks; and iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. This DGAD survey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in anomaly detection in dynamic graphs. Keywords: Graphs, Anomaly Detection, dynamic networks,Graph Neural Networks (GNN), Node anomaly, Graph mining.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 86 of its 184 references that are in the map. First posted on arXiv 31 May 2024.

Builds on

86 of 184 references.

References · 86

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 9.

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  1. 2024A Generalizable Anomaly Detection Method in Dynamic Graphscited by 10
  2. 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspectivecited by 4
  3. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  4. 2026An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphscited by 2
  5. 2024Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphscited by 1
  6. 2025T3former: Temporal Graph Classification with Topological Machine Learningcited by 1
  7. 2025Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphscited by 1
  8. 2026Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streamscited by 1
  9. 2025Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networkscited by 0
  10. 2025Predicting Graph Structure via Adapted Flux Balance Analysiscited by 0
  11. 2025Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decaycited by 0
  12. 2025Debugging Tabular Log as Dynamic Graphscited by 0
6 more
  1. 2026TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Predictioncited by 0
  2. 2026A Modular LLM Framework for Explainable Price Outlier Detectioncited by 0
  3. 2026Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detectioncited by 0
  4. 2026Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectorscited by 0
  5. 2026Topology-induced Operators Reveal Complementary Graph Representations without Trainingcited by 0
  6. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 0