Context-aware Event Forecasting via Graph Disentanglement

TL;DR

This paper introduces SeCoGD, a novel graph disentanglement framework for context-aware event forecasting that leverages auxiliary contextual information to improve prediction accuracy, addressing limitations of existing structured models.

Contribution

The paper proposes a new task of context-aware event forecasting and develops SeCoGD, a graph disentanglement model that effectively integrates multi-context information for better predictions.

Findings

SeCoGD outperforms state-of-the-art methods on GDELT-based datasets.

Constructed three large-scale datasets for the new task.

Demonstrated the importance of contextual information in event forecasting.

Abstract

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.