Graph similarity learning for change-point detection in dynamic networks
- URL: http://arxiv.org/abs/2203.15470v1
- Date: Tue, 29 Mar 2022 12:16:38 GMT
- Title: Graph similarity learning for change-point detection in dynamic networks
- Authors: Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong
- Abstract summary: We consider dynamic networks that are temporal sequences of graph snapshots.
This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring.
We design a method to perform online network change-point detection that can adapt to the specific network domain and localise changes with no delay.
- Score: 15.694880385913534
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dynamic networks are ubiquitous for modelling sequential graph-structured
data, e.g., brain connectome, population flows and messages exchanges. In this
work, we consider dynamic networks that are temporal sequences of graph
snapshots, and aim at detecting abrupt changes in their structure. This task is
often termed network change-point detection and has numerous applications, such
as fraud detection or physical motion monitoring. Leveraging a graph neural
network model, we design a method to perform online network change-point
detection that can adapt to the specific network domain and localise changes
with no delay. The main novelty of our method is to use a siamese graph neural
network architecture for learning a data-driven graph similarity function,
which allows to effectively compare the current graph and its recent history.
Importantly, our method does not require prior knowledge on the network
generative distribution and is agnostic to the type of change-points; moreover,
it can be applied to a large variety of networks, that include for instance
edge weights and node attributes. We show on synthetic and real data that our
method enjoys a number of benefits: it is able to learn an adequate graph
similarity function for performing online network change-point detection in
diverse types of change-point settings, and requires a shorter data history to
detect changes than most existing state-of-the-art baselines.
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