# GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs

arXiv: https://arxiv.org/abs/2607.23556 · HTML: https://mlsanity.com/paper/2607.23556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation](https://mlsanity.com/paper/2405.18106/) (2024)
- [Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation](https://mlsanity.com/paper/2304.07503/) (2023)
- [Retrofitting Temporal Graph Neural Networks with Transformer](https://mlsanity.com/paper/2409.05477/) (2024)
- [Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph](https://mlsanity.com/paper/2501.08653/) (2025)
- [Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal Knowledge Graph Framework](https://mlsanity.com/paper/2412.12385/) (2024)

## Abstract (arXiv)

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 occurrence time. Empirical evaluations across multiple datasets demonstrate that our method consistently outperforms existing techniques, particularly in scenarios involving irregular event patterns and complex temporal dependencies. These findings highlight the potential of our framework as a robust foundation for future research in temporal event prediction.