“Neighbourhood matching creates realistic surrogate temporal networks”

A promising solution is ‘surrogate networks’, synthetic graphs with the properties of real-world networks.
Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, as well as their correlations, in a scalable model.
In this seminar, we propose a novel and simple method for generating surrogate temporal networks.
By decomposing graphs into temporal neighborhoods surrounding each node, we can generate new networks using neighborhoods as building blocks.
The model vastly outperforms current methods across multiple examples of temporal networks in terms of both topological and dynamical similarity.
It will be further show that beyond generating realistic interaction patterns, our method is able to capture intrinsic temporal periodicity of temporal networks, all with an execution time lower than competing methods by multiple orders of magnitude.

Bio del relatore: Antonio Longa is a PhD student at the Fondazione Bruno Kessler (Digital Society Center) and the University of Trento, under the supervision of Bruno Lepri and Andrea Passerini. He is currently a visiting researcher at Cambridge University (Cambridge, UK). His research mainly focuses on Temporal Graph Mining and Geometric Deep Learning. Before joining the Fondazione Bruno Kessler, he studied as an exchange student at Aalto University (Finland) and he did his master thesis at the University of Exeter (UK). He received his master’s degree from the University of Trento and his bachelor’s degree from Milano-Bicocca University.

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