# Predicting Influential Higher-Order Patterns in Temporal Network Data

@article{Gote2021PredictingIH, title={Predicting Influential Higher-Order Patterns in Temporal Network Data}, author={Christoph Gote and Vincenzo Perri and Ingo Scholtes}, journal={ArXiv}, year={2021}, volume={abs/2107.12100} }

Networks are frequently used to model complex systems comprised of interacting elements. While links capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly in uence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. However, to avoid over tting, such models should only consider those higher-order patterns for which the… Expand

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