# Context-aware Event Forecasting via Graph Disentanglement

arXiv: https://arxiv.org/abs/2308.06480 · HTML: https://mlsanity.com/paper/2308.06480/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2023.

## Built on by (most cited first, 10 of 10)

- [OpenEP: Open-Ended Future Event Prediction](https://mlsanity.com/paper/2408.06578/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting](https://mlsanity.com/paper/2407.11638/) (2024)
- [MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models](https://mlsanity.com/paper/2408.04388/) (2024)
- [AttackSeqBench: Benchmarking the Capabilities of LLMs for Attack Sequences Understanding](https://mlsanity.com/paper/2503.03170/) (2025)
- [Beyond Pairwise: Global Zero-shot Temporal Graph Generation](https://mlsanity.com/paper/2502.11114/) (2025)
- [EventFull: Complete and Consistent Event Relation Annotation](https://mlsanity.com/paper/2412.12733/) (2024)
- [Large Language Models as Interpolated and Extrapolated Event Predictors](https://mlsanity.com/paper/2406.10492/) (2024)
- [ForecastAgentSearch: Towards a Multi-Expert Agent Search System for Geopolitical Event Forecasting](https://mlsanity.com/paper/2606.31665/) (2026)
- [Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.13153/) (2026)
- [From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models](https://mlsanity.com/paper/2607.27654/) (2026)

## Abstract (arXiv)

Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. Since there is no available dataset for this novel task, we construct three large-scale datasets based on GDELT. Experimental results demonstrate that our model outperforms a list of SOTA methods.