Temporal networks are mathematical tools used to model complex systems which embed the temporal dimension. In this thesis we address the problem of counting motifs in temporal networks. We provide a new exact parallel algorithm which is both scalable and efficient in practice. We address the problem of approximating an exact count with rigorous guarantees. We provide, to the best of our knowledge, the first rigorous sampling algorithms devised for such task.

Mining motifs in temporal networks

Sarpe, Ilie
2019/2020

Abstract

Temporal networks are mathematical tools used to model complex systems which embed the temporal dimension. In this thesis we address the problem of counting motifs in temporal networks. We provide a new exact parallel algorithm which is both scalable and efficient in practice. We address the problem of approximating an exact count with rigorous guarantees. We provide, to the best of our knowledge, the first rigorous sampling algorithms devised for such task.
2019-09-10
data-mining, graphs, algorithms, Hoeffding, Martingales
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/23975