Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge gr...

We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules. Motivated by recent work proposing a strong baseline based on recurrent fac...

Temporal networks are powerful models for understanding dynamical complex systems where entities and interactions appear and vanish as time goes by. Unlike static networks, which simply provide a snap...

Temporal networks are powerful models for understanding dynamical complex systems where entities and interactions appear and vanish as time goes by. Unlike static networks, which simply provide a snap...

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This paper presents a novel approach to understanding global crises and trade patterns through the creation and analysis of a Temporal Knowledge Graph (TKG), and the application of Temporal...