# Changepoint Detection in Highly-Attributed Dynamic Graphs

arXiv: https://arxiv.org/abs/2407.06998 · HTML: https://mlsanity.com/paper/2407.06998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 9 Jul 2024.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Structure and inference in annotated networks](https://mlsanity.com/paper/1507.04001/) (2015)
- [vGraph: A Generative Model for Joint Community Detection and Node Representation Learning](https://mlsanity.com/paper/1906.07159/) (2019)
- [Laplacian Change Point Detection for Dynamic Graphs](https://mlsanity.com/paper/2007.01229/) (2020)
- [Fast Change Point Detection on Dynamic Social Networks](https://mlsanity.com/paper/1705.07325/) (2017)
- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [Dynamic latent space relational event model](https://mlsanity.com/paper/2204.04753/) (2022)
- [CS-TGN: Community Search via Temporal Graph Neural Networks](https://mlsanity.com/paper/2303.08964/) (2023)
- [Change Detection in a Dynamic Stream of Attributed Networks](https://mlsanity.com/paper/1711.04441/) (2017)
- [Online network monitoring](https://mlsanity.com/paper/2010.09398/) (2020)

## Abstract (arXiv)

Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network's modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot's modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the #Iran Twitter reply network, where each node has high-dimensional textual attributes.