# Change Detection in a Dynamic Stream of Attributed Networks

arXiv: https://arxiv.org/abs/1711.04441 · HTML: https://mlsanity.com/paper/1711.04441/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (2025)
- [Nonlinear State Space Modeling and Control of the Impact of Patients' Modifiable Lifestyle Behaviors on the Emergence of Multiple Chronic Conditions](https://mlsanity.com/paper/2107.13394/) (2021)
- [Changepoint Detection in Highly-Attributed Dynamic Graphs](https://mlsanity.com/paper/2407.06998/) (2024)

## Abstract (arXiv)

While anomaly detection in static networks has been extensively studied, only recently, researchers have focused on dynamic networks. This trend is mainly due to the capacity of dynamic networks in representing complex physical, biological, cyber, and social systems. This paper proposes a new methodology for modeling and monitoring of dynamic attributed networks for quick detection of temporal changes in network structures. In this methodology, the generalized linear model (GLM) is used to model static attributed networks. This model is then combined with a state transition equation to capture the dynamic behavior of the system. Extended Kalman filter (EKF) is used as an online, recursive inference procedure to predict and update network parameters over time. In order to detect changes in the underlying mechanism of edge formation, prediction residuals are monitored through an Exponentially Weighted Moving Average (EWMA) control chart. The proposed modeling and monitoring procedure is examined through simulations for attributed binary and weighted networks. The email communication data from the Enron corporation is used as a case study to show how the method can be applied in real-world problems.