# F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams

arXiv: https://arxiv.org/abs/2011.04723 · HTML: https://mlsanity.com/paper/2011.04723/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Neighborhood-aware Scalable Temporal Network Representation Learning](https://mlsanity.com/paper/2209.01084/) (2022)
- [Alleviating Structural Distribution Shift in Graph Anomaly Detection](https://mlsanity.com/paper/2401.14155/) (2024)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Anomalous Edge Detection in Edge Exchangeable Social Network Models](https://mlsanity.com/paper/2109.12727/) (2021)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning](https://mlsanity.com/paper/2402.11933/) (2024)
- [Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction](https://mlsanity.com/paper/2407.20871/) (2024)
- [Sketch-Based Anomaly Detection in Streaming Graphs](https://mlsanity.com/paper/2106.04486/) (2021)
- [DyGKT: Dynamic Graph Learning for Knowledge Tracing](https://mlsanity.com/paper/2407.20824/) (2024)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs](https://mlsanity.com/paper/2106.15504/) (2021)
- [Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling](https://mlsanity.com/paper/2402.01964/) (2024)
- [Isconna: Streaming Anomaly Detection with Frequency and Patterns](https://mlsanity.com/paper/2104.01632/) (2021)
- [Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability](https://mlsanity.com/paper/2408.00872/) (2024)
- [Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts](https://mlsanity.com/paper/2504.00328/) (2025)
- [ARES: Anomaly Recognition Model For Edge Streams](https://mlsanity.com/paper/2511.22078/) (2025)
- [tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs](https://mlsanity.com/paper/2210.08847/) (2022)
- [A Frequency-Structure Approach for Link Stream Analysis](https://mlsanity.com/paper/2212.03804/) (2022)
- [Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication](https://mlsanity.com/paper/2501.17329/) (2025)
- [Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo](https://mlsanity.com/paper/2504.17252/) (2025)
- [Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay](https://mlsanity.com/paper/2509.11633/) (2025)
- [LoReTTA: A Low Resource Framework To Poison Continuous Time Dynamic Graphs](https://mlsanity.com/paper/2511.07379/) (2025)
- [Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies](https://mlsanity.com/paper/2603.01841/) (2026)
- [Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors](https://mlsanity.com/paper/2608.15965/) (2026)

## Abstract (arXiv)

Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks. The problem of detecting anomalies or rare events in edge streams has a wide range of applications. However, it presents many challenges due to lack of labels, a highly dynamic nature of interactions, and the entanglement of temporal and structural changes in the network. Current methods are limited in their ability to address the above challenges and to efficiently process a large number of interactions. Here, we propose F-FADE, a new approach for detection of anomalies in edge streams, which uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. The anomalies are then determined based on the likelihood of the observed frequency of each incoming interaction. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory. Our experiments on one synthetic and six real-world dynamic networks show that F-FADE achieves state of the art performance and may detect anomalies that previous methods are unable to find.