# SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning

arXiv: https://arxiv.org/abs/2402.11933 · HTML: https://mlsanity.com/paper/2402.11933/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Do We Really Need Complicated Model Architectures For Temporal Networks?](https://mlsanity.com/paper/2302.11636/) (2023)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding](https://mlsanity.com/paper/2011.11545/) (2020)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams](https://mlsanity.com/paper/1911.04464/) (2019)
- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams](https://mlsanity.com/paper/2011.04723/) (2020)
- [Shuffling Recurrent Neural Networks](https://mlsanity.com/paper/2007.07324/) (2020)
- [Sketch-Based Anomaly Detection in Streaming Graphs](https://mlsanity.com/paper/2106.04486/) (2021)
- [Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation](https://mlsanity.com/paper/2304.07503/) (2023)
- [Representation Learning in Continuous-Time Dynamic Signed Networks](https://mlsanity.com/paper/2207.03408/) (2022)

## Built on by (most cited first, 14 of 14)

- [Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection](https://mlsanity.com/paper/2507.11997/) (2025)
- [DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes](https://mlsanity.com/paper/2508.00664/) (2025)
- [Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective](https://mlsanity.com/paper/2608.18104/) (2026)
- [Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts](https://mlsanity.com/paper/2504.00328/) (2025)
- [ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks](https://mlsanity.com/paper/2508.20829/) (2025)
- [ARES: Anomaly Recognition Model For Edge Streams](https://mlsanity.com/paper/2511.22078/) (2025)
- [Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection](https://mlsanity.com/paper/2605.27470/) (2026)
- [TiGer: Self-Supervised Purification for Time-evolving Graphs](https://mlsanity.com/paper/2503.06990/) (2025)
- [Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs](https://mlsanity.com/paper/2506.07417/) (2025)
- [Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies](https://mlsanity.com/paper/2603.01841/) (2026)
- [Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks](https://mlsanity.com/paper/2605.13863/) (2026)
- [Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectors](https://mlsanity.com/paper/2608.15559/) (2026)
- [Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors](https://mlsanity.com/paper/2608.15965/) (2026)
- [Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection](https://mlsanity.com/paper/2608.19858/) (2026)

## Abstract (arXiv)

To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) adapting to dynamically changing states, and (c) handling the scarcity of dynamic anomaly labels. In this paper, we propose SLADE (Self-supervised Learning for Anomaly Detection in Edge Streams) for rapid detection of dynamic anomalies in edge streams, without relying on labels. SLADE detects the shifts of nodes into abnormal states by observing deviations in their interaction patterns over time. To this end, it trains a deep neural network to perform two self-supervised tasks: (a) minimizing drift in node representations and (b) generating long-term interaction patterns from short-term ones. Failure in these tasks for a node signals its deviation from the norm. Notably, the neural network and tasks are carefully designed so that all required operations can be performed in constant time (w.r.t. the graph size) in response to each new edge in the input stream. In dynamic anomaly detection across four real-world datasets, SLADE outperforms nine competing methods, even those leveraging label supervision.