Temporal Motif

Temporal motifs are recurring patterns of interactions within time-ordered data, such as sequences of events in a network or time series. Research focuses on efficiently identifying these motifs, even in large datasets and with variations in timing or structure, using techniques like matrix profile analysis and specialized graph neural networks. This work is crucial for understanding complex dynamic systems across diverse fields, improving model explainability, and enabling more accurate predictions in areas like fraud detection and financial modeling. The development of scalable algorithms and novel evaluation metrics is driving progress in this rapidly evolving area.