@article{sulem2023_graph_change_point,
	abstract = {Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectivity, population migrations, and social networks. In this work, we consider the discrete-time framework of dynamic networks and aim at detecting change-points, i.e., abrupt changes in the structure or attributes of the graph snapshots. This task is often termed network change-point detection and has numerous applications, such as market phase discovery, fraud detection, and activity monitoring. In this work, we propose a data-driven method that can adapt to the specific network domain, and be used to detect distribution changes with no delay and in an online setting. Our algorithm is based on a siamese graph neural network, designed to learn a graph similarity function on the graph snapshots from the temporal network sequence. Without any prior knowledge on the network generative distribution and the type of change-points, our learnt similarity function allows to more effectively compare the current graph and its recent history, compared to standard graph distances or kernels. Moreover, our method can be applied to a large variety of network data, e.g., networks with edge weights or node attributes. We test our method on synthetic and real-world dynamic network data, and demonstrate that it is able to perform online network change-point detection in diverse settings. Besides, we show that it requires a shorter data history to detect changes than most existing state-of-the-art baselines.},
	author = {Sulem, D{\'e}borah and Kenlay, Henry and Cucuringu, Mihai and Dong, Xiaowen},
	date = {2023/10/31},
	date-added = {2023-11-04 18:07:07 +0000},
	date-modified = {2023-11-04 18:07:07 +0000},
	doi = {10.1007/s10994-023-06405-x},
	id = {Sulem2023},
	isbn = {1573-0565},
	journal = {Machine Learning},
	title = {Graph similarity learning for change-point detection in dynamic networks},
	url = {https://doi.org/10.1007/s10994-023-06405-x},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1007/s10994-023-06405-x}
}