GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs
Temporal graphs are increasingly used to model dynamic systems in diverse domains such as social networks, financial networks, and traffic networks. Predicting both what the next event will be and when it will occur in these systems is crucial for understanding and anticipating complex behaviors, but has not been studied much. To address this gap, we propose a unified mathematical framework capable of capturing varying degrees of complexity across temporal graphs. Our framework is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics. Building upon this analysis, we introduce our novel approach for jointly predicting the next event and its ...
| Authors | Mohammad Ostadmohammadi, Sepehr Kazemi, Hamid R. Rabiee |
|---|---|
| Primary category | cs.SI |
| Published | 26 July 2026 |
| Last updated at source | 14 August 2026 |
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