# Fast Change Point Detection on Dynamic Social Networks

arXiv: https://arxiv.org/abs/1705.07325 · HTML: https://mlsanity.com/paper/1705.07325/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2017.

## Built on by (most cited first, 11 of 11)

- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition](https://mlsanity.com/paper/2106.16042/) (2021)
- [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)
- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (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)
- [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)
- [Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay](https://mlsanity.com/paper/2509.11633/) (2025)
- [Conditional Entropy of Heat Diffusion on Temporal Networks](https://mlsanity.com/paper/2605.21514/) (2026)

## Abstract (arXiv)

A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.