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Communication Dans Un Congrès Année : 2023

Link Streams as a Generalization of Graphs and Time Series

Résumé

A link stream is a set of possibly weighted triplets (t, u, v) modeling that u and v interacted at time t. Link streams offer an effective model for datasets containing both temporal and relational information, making their proper analysis crucial in many applications. They are commonly regarded as sequences of graphs or collections of time series. Yet, a recent seminal work demonstrated that link streams are more general objects of which graphs are only particular cases. It therefore started the construction of a dedicated formalism for link streams by extending graph theory. In this work, we contribute to the development of this formalism by showing that link streams also generalize time series. In particular, we show that a link stream corresponds to a time-series extended to a relational dimension, which opens the door to also extend the framework of signal processing to link streams. We therefore develop extensions of numerous signal concepts to link streams: from elementary ones like energy, correlation, and differentiation, to more advanced ones like Fourier transform and filters.
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Dates et versions

hal-04481079 , version 1 (27-02-2024)

Identifiants

Citer

Esteban Bautista, Matthieu Latapy. Link Streams as a Generalization of Graphs and Time Series. 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), Nov 2023, Atlanta, GA, United States. pp.150-158, ⟨10.1109/CogMI58952.2023.00030⟩. ⟨hal-04481079⟩
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