# Latent Evolution Model for Change Point Detection in Time-varying Networks

arXiv: https://arxiv.org/abs/2212.08818 · HTML: https://mlsanity.com/paper/2212.08818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Laplacian Change Point Detection for Dynamic Graphs](https://mlsanity.com/paper/2007.01229/) (2020)
- [Latent Space Model for Road Networks to Predict Time-Varying Traffic](https://mlsanity.com/paper/1602.04301/) (2016)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Detecting change points in the large-scale structure of evolving networks](https://mlsanity.com/paper/1403.0989/) (2014)
- [Fast Change Point Detection on Dynamic Social Networks](https://mlsanity.com/paper/1705.07325/) (2017)
- [Optimal Change Point Detection and Localization in Sparse Dynamic Networks](https://mlsanity.com/paper/1809.09602/) (2018)
- [Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development](https://mlsanity.com/paper/1912.03440/) (2019)
- [Field-wise Learning for Multi-field Categorical Data](https://mlsanity.com/paper/2012.00202/) (2020)
- [Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network](https://mlsanity.com/paper/2101.07425/) (2021)
- [Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points](https://mlsanity.com/paper/1907.01162/) (2019)
- [Series Photo Selection via Multi-view Graph Learning](https://mlsanity.com/paper/2203.09736/) (2022)
- [A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments](https://mlsanity.com/paper/1810.07776/) (2018)

## Built on by (most cited first, 1 of 1)

- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (2025)

## Abstract (arXiv)

Graph-based change point detection (CPD) play an irreplaceable role in discovering anomalous graphs in the time-varying network. While several techniques have been proposed to detect change points by identifying whether there is a significant difference between the target network and successive previous ones, they neglect the natural evolution of the network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. Considering this problem, we treat the problem as a prediction task and propose a novel CPD method for dynamic graphs via a latent evolution model. Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously. After having the evolving patterns, a prediction of the target network can be achieved. Then, we can detect the change points by comparing the prediction and the actual network by leveraging a trade-off strategy, which balances the importance between the prediction network and the normal graph pattern extracted from previous networks. Intensive experiments conducted on both synthetic and real-world datasets show the effectiveness and superiority of our model.