Stochastic Event Prediction via Temporal Motif Transitions
Networks of timestamped interactions arise across social, financial, and biological domains, where forecasting future events requires modeling both evolving topology and temporal ordering. Temporal link prediction methods typically frame the task as binary classification with negative sampling, discarding the sequential and correlated nature of real-world interactions. We introduce STEP (STochastic Event Predictor), a framework that reformulates temporal link prediction as a sequential forecasting problem in continuous time. STEP models event dynamics through discrete temporal motif transitions governed by Poisson processes, maintaining a set of open motif instances that evolve as new int...
| Authors | İbrahim Bahadır Altun, Ahmet Erdem Sarıyüce |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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