Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment

Title

Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment

Creator

Mudassir Alam, N.; Batra, Umang; George, Shiju

Description

The paper examines the application of the Graph Neural Networks (GNNs) to the ICEWS14 temporal knowledge graph to predict geopolitical events and relationship. We suggest a new architecture that involves negative sampling and temporal encoding trick that enhances performance on temporal link prediction. Our model combines a central temporal-conscious attention GNN with numerous domain-specific GNN sub-models that are taught on economics trends, political choices, and business interrelations and it additionally incorporates knowledge graphs, sentiment analysis of the people, and market tendencies data to provide comprehensive consulting assistance. The framework has an AUCROC of 0.9234, test set, indicating that the framework is more likely to detect both positive and negative links. Exploring the high-confidence predictions, we identify the regularities of the political enforcement action, whereas the low-confidence ones assist in suspecting the improbable or counterfeit connections. Such explanations are useful in designing early warning systems, risk-assessment monitors, and decision-support frameworks in foreign affairs and think tank consultancy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1928 LNNS;pp.140-152

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Event Prediction; Geopolitical Forecasting; Graph Neural Networks; ICEWS14; Negative Sampling; Temporal Knowledge Graph

Coverage

Mudassir Alam N., Christ University, Karnataka, Bangalore, India; Batra U., Christ University, Karnataka, Bangalore, India; George S., Christ University, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303222916-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-22917-5_12

https://www.scopus.com/pages/publications/105039515933?origin=resultslist