arXiv cs.AI· Mohammad Ostadmohammadi, Sepehr Kazemi, Hamid R. Rabiee·· 14 小时前AI 评分22
GTIN：面向时序图事件与时间联合预测的统一可扩展框架
GTIN: A Unified Scalable Framework for Joint Event and Time Prediction in Temporal Graphs
AI 导读
GTIN 是一个可扩展的统一数学框架，用于在边事件时序图中联合预测下一个事件及其发生时间。该框架可适配多种网络结构与时间动态，在多个数据集上的实验表明其持续优于现有方法，尤其在不规则事件模式和复杂时间依赖场景下表现突出。
正文 · 原文
Abstract:Temporal graphs are increasingly used to model dynamic systems across diverse domains, including social, financial, 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 yet, especially in large-scale graphs. To address this gap, we propose a scalable unified mathematical framework that captures varying degrees of complexity across temporal graphs with events occurring on the edges. 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 scalable 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.
| Subjects: | Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.23556 [cs.SI] |
| (or arXiv:2607.23556v3 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.23556 arXiv-issued DOI via DataCite |
Submission history
From: Mohammad Ostadmohammadi [view email]
[v1]
Sun, 26 Jul 2026 09:15:27 UTC (222 KB)
[v2]
Fri, 14 Aug 2026 15:09:18 UTC (218 KB)
[v3]
Mon, 26 Oct 2026 07:24:19 UTC (160 KB)
来源：arXiv cs.AI · arxiv.org