# Community detection and anomaly prediction in dynamic networks

arXiv: https://arxiv.org/abs/2404.10468 · HTML: https://mlsanity.com/paper/2404.10468/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Non-Negative Matrix Factorization for Event Data](https://mlsanity.com/paper/2606.06205/) (2026)
- [Diffusion-Induced Spatial Attention Overlapping Community Detection](https://mlsanity.com/paper/2609.26737/) (2026)

## Abstract (arXiv)

Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks.