Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mudassir Alam, N.; Batra, Umang; George, Shiju Title A name given to the resource Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1928 LNNS;pp.140-152 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-22917-5_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-22917-5_12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039515933?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039515933?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mudassir Alam N., Christ University, Karnataka, Bangalore, India; Batra U., Christ University, Karnataka, Bangalore, India; George S., Christ University, Karnataka, Bangalore, India Description An account of the resource 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. Subject The topic of the resource Event Prediction; Geopolitical Forecasting; Graph Neural Networks; ICEWS14; Negative Sampling; Temporal Knowledge Graph Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303222916-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online