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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 <jats:italic>network change-point detection<\/jats:italic> 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 <jats:italic>online<\/jats:italic> setting. Our algorithm is based on a <jats:italic>siamese graph neural network<\/jats:italic>, 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.<\/jats:p>","DOI":"10.1007\/s10994-023-06405-x","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T21:33:45Z","timestamp":1698788025000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Graph similarity learning for change-point detection in dynamic networks"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4781-4848","authenticated-orcid":false,"given":"D\u00e9borah","family":"Sulem","sequence":"first","affiliation":[]},{"given":"Henry","family":"Kenlay","sequence":"additional","affiliation":[]},{"given":"Mihai","family":"Cucuringu","sequence":"additional","affiliation":[]},{"given":"Xiaowen","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"6405_CR1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau4996","author":"K Runge","year":"2019","unstructured":"Runge, K., et al. 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