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arXiv · 2407.06998

Changepoint Detection in Highly-Attributed Dynamic Graphs

Emiliano Penaloza·Nathaniel Stevens
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Abstract

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.

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Emiliano Penaloza, Nathaniel Stevens. 2024-07-09. Changepoint Detection in Highly-Attributed Dynamic Graphs. https://arxiv.org/abs/2407.06998

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