Changepoint Detection in Highly-Attributed Dynamic Graphs
<|reference_start|>Changepoint Detection in Highly-Attributed Dynamic Graphs: 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.<|reference_end|>
@article{penaloza2024changepoint,
    title={Changepoint Detection in Highly-Attributed Dynamic Graphs},
    author={Emiliano Penaloza, Nathaniel Stevens},
    journal={arXiv preprint arXiv:2407.06998},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06998},
    primaryClass={cs.SI cs.LG}
}