Changepoint Detection in Highly-Attributed Dynamic Graphs
This addresses the problem of changepoint detection in attributed dynamic graphs for applications like social network monitoring, but it is incremental as it adapts existing GNN methods to a specific task.
The paper tackled detecting anomalous behavior in dynamic networks with high-dimensional node attributes by tracking modularity using Graph Neural Networks, and demonstrated its ability to detect changes in simulations and a real-world Twitter event.
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.