# Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs

arXiv: https://arxiv.org/abs/2302.01204 · HTML: https://mlsanity.com/paper/2302.01204/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 65 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Structure and inference in annotated networks](https://mlsanity.com/paper/1507.04001/) (2015)
- [Laplacian Change Point Detection for Dynamic Graphs](https://mlsanity.com/paper/2007.01229/) (2020)
- [Detecting change points in the large-scale structure of evolving networks](https://mlsanity.com/paper/1403.0989/) (2014)
- [Network Density of States](https://mlsanity.com/paper/1905.09758/) (2019)
- [Fast Change Point Detection on Dynamic Social Networks](https://mlsanity.com/paper/1705.07325/) (2017)
- [The Laplacian Spectrum of Large Graphs Sampled from Graphons](https://mlsanity.com/paper/2004.09177/) (2020)
- [The Power Mean Laplacian for Multilayer Graph Clustering](https://mlsanity.com/paper/1803.00491/) (2018)
- [Constant State of Change: Engagement Inequality in Temporal Dynamic Networks](https://mlsanity.com/paper/1910.01722/) (2019)
- [Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding](https://mlsanity.com/paper/1705.03877/) (2017)
- [Hierarchical Change Point Detection on Dynamic Networks](https://mlsanity.com/paper/1706.02186/) (2017)

## Built on by (most cited first, 7 of 7)

- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs](https://mlsanity.com/paper/2408.06966/) (2024)
- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (2025)
- [Multiresolution Analysis and Statistical Thresholding on Dynamic Networks](https://mlsanity.com/paper/2506.01208/) (2025)
- [T3former: Temporal Graph Classification with Topological Machine Learning](https://mlsanity.com/paper/2510.13789/) (2025)
- [A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems](https://mlsanity.com/paper/2401.10300/) (2024)
- [Conditional Entropy of Heat Diffusion on Temporal Networks](https://mlsanity.com/paper/2605.21514/) (2026)

## Abstract (arXiv)

Dynamic 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 three main challenges associated with this problem: i). how to compare graph snapshots across time, ii). how to capture temporal dependencies, and iii). how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i). LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii). MultiLAD's advantage over contenders significantly increases when additional views are available, and iii). MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.