# DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes

arXiv: https://arxiv.org/abs/2508.00664 · HTML: https://mlsanity.com/paper/2508.00664/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 69 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 30 of 31)

- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://mlsanity.com/paper/2302.08043/) (2023)
- [Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts](https://mlsanity.com/paper/2403.06495/) (2024)
- [All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining](https://mlsanity.com/paper/2402.09834/) (2024)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2005.07427/) (2020)
- [Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2502.07601/) (2025)
- [ARC: A Generalist Graph Anomaly Detector with In-Context Learning](https://mlsanity.com/paper/2405.16771/) (2024)
- [Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation](https://mlsanity.com/paper/2108.06468/) (2021)
- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts](https://mlsanity.com/paper/2410.14886/) (2024)
- [Dynamically Expandable Graph Convolution for Streaming Recommendation](https://mlsanity.com/paper/2303.11700/) (2023)
- [A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection](https://mlsanity.com/paper/2502.13308/) (2025)
- [AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection](https://mlsanity.com/paper/2502.09254/) (2025)
- [CausalFormer: An Interpretable Transformer for Temporal Causal Discovery](https://mlsanity.com/paper/2406.16708/) (2024)
- [DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs](https://mlsanity.com/paper/2408.06966/) (2024)
- [SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning](https://mlsanity.com/paper/2402.11933/) (2024)
- [CLDG: Contrastive Learning on Dynamic Graphs](https://mlsanity.com/paper/2412.14451/) (2024)
- [Subset Node Anomaly Tracking over Large Dynamic Graphs](https://mlsanity.com/paper/2205.09786/) (2022)
- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis](https://mlsanity.com/paper/2408.13082/) (2024)
- [Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks](https://mlsanity.com/paper/2402.12411/) (2024)
- [Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective](https://mlsanity.com/paper/2312.14329/) (2023)
- [FineRec:Exploring Fine-grained Sequential Recommendation](https://mlsanity.com/paper/2404.12975/) (2024)
- [Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks](https://mlsanity.com/paper/2407.01979/) (2024)
- [GCAL: Adapting Graph Models to Evolving Domain Shifts](https://mlsanity.com/paper/2505.16860/) (2025)
- [Revisiting Dynamic Graph Clustering via Matrix Factorization](https://mlsanity.com/paper/2502.06117/) (2025)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [Retrieval Augmented Generation for Dynamic Graph Modeling](https://mlsanity.com/paper/2408.14523/) (2024)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)

## Built on by (most cited first, 5 of 5)

- [Glocal Information Bottleneck for Time Series Imputation](https://mlsanity.com/paper/2510.04910/) (2025)
- [Node Role-Guided LLMs for Dynamic Graph Clustering](https://mlsanity.com/paper/2603.13799/) (2026)
- [Explaining Synergistic Effects in Social Recommendations](https://mlsanity.com/paper/2601.18151/) (2026)
- [Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection](https://mlsanity.com/paper/2608.19858/) (2026)
- [Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context](https://mlsanity.com/paper/2610.19364/) (2026)

## Abstract (arXiv)

Dynamic graph anomaly detection (DGAD) is essential for identifying anomalies in evolving graphs across domains such as finance, traffic, and social networks. Recently, generalist graph anomaly detection (GAD) models have shown promising results. They are pretrained on multiple source datasets and generalize across domains. While effective on static graphs, they struggle to capture evolving anomalies in dynamic graphs. Moreover, the continuous emergence of new domains and the lack of labeled data further challenge generalist DGAD. Effective cross-domain DGAD requires both domain-specific and domain-agnostic anomalous patterns. Importantly, these patterns evolve temporally within and across domains. Building on these insights, we propose a DGAD model with Dynamic Prototypes (DP) to capture evolving domain-specific and domain-agnostic patterns. Firstly, DP-DGAD extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer is selectively updated to retain general, domain-agnostic patterns while incorporating new domain-specific ones. Then, an anomaly scorer compares incoming data with dynamic prototypes to flag both general and domain-specific anomalies. Finally, DP-DGAD employs confidence-based pseudo-labeling for effective self-supervised adaptation in target domains. Extensive experiments demonstrate state-of-the-art performance across ten real-world datasets from different domains.