# Laplacian Change Point Detection for Dynamic Graphs

arXiv: https://arxiv.org/abs/2007.01229 · HTML: https://mlsanity.com/paper/2007.01229/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 25 of 25)

- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery](https://mlsanity.com/paper/2310.19324/) (2023)
- [A survey of dynamic graph neural networks](https://mlsanity.com/paper/2404.18211/) (2024)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.16385/) (2023)
- [Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck](https://mlsanity.com/paper/2406.13214/) (2024)
- [Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs](https://mlsanity.com/paper/2302.01204/) (2023)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning](https://mlsanity.com/paper/2406.04897/) (2024)
- [Leveraging Temporal Graph Networks Using Module Decoupling](https://mlsanity.com/paper/2310.02721/) (2023)
- [Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning](https://mlsanity.com/paper/2504.08129/) (2025)
- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (2025)
- [FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction](https://mlsanity.com/paper/2511.18631/) (2025)
- [Latent Evolution Model for Change Point Detection in Time-varying Networks](https://mlsanity.com/paper/2212.08818/) (2022)
- [Fast and Attributed Change Detection on Dynamic Graphs with Density of States](https://mlsanity.com/paper/2305.08750/) (2023)
- [Curriculum Negative Mining For Temporal Networks](https://mlsanity.com/paper/2407.17070/) (2024)
- [Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2410.05687/) (2024)
- [Static and Dynamic Approaches to Computing Barycenters of Probability Measures on Graphs](https://mlsanity.com/paper/2603.26940/) (2026)
- [Anomaly detection in dynamic networks](https://mlsanity.com/paper/2210.07407/) (2022)
- [Balancing Summarization and Change Detection in Graph Streams](https://mlsanity.com/paper/2311.18694/) (2023)
- [A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems](https://mlsanity.com/paper/2401.10300/) (2024)
- [Changepoint Detection in Highly-Attributed Dynamic Graphs](https://mlsanity.com/paper/2407.06998/) (2024)
- [Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix](https://mlsanity.com/paper/2605.16112/) (2026)
- [Conditional Entropy of Heat Diffusion on Temporal Networks](https://mlsanity.com/paper/2605.21514/) (2026)
- [Geodesics of Dynamic Graphs for Regime Change Detection](https://mlsanity.com/paper/2606.07151/) (2026)

## Abstract (arXiv)

Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. LAD explicitly models short term and long term dependencies by applying two sliding windows. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network. In all three datasets, we demonstrate that our method can more effectively identify anomalous time points according to significant real world events.