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 Anandu, P.S.; Gogi, Vyshali J.; Azarudheen, S. Title A name given to the resource FINNET: A Hierarchical Graph Learning Framework for Adaptive Cross-Market Financial Risk Prediction 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 Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IATMSI68868.2026.11466022" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IATMSI68868.2026.11466022</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036968997?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036968997?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 Anandu P.S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Gogi V.J., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Azarudheen S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India Description An account of the resource Systemic financial risk emerges from complex multi-scale interactions among entities, sectors, and markets. We introduce FINNET, a hierarchical graph neural network framework that models these vertical dependencies through volatility-aware adaptive pooling. Our approach features: (1) a tri-scale graph structure capturing entity, sector, and market dynamics; (2) dynamic embeddings combining static features with time-varying signals; (3) transfer learning for emerging markets; and (4) transparent risk decomposition for regulatory compliance. Validated on 58,432 financial entities across three continents, FINNET achieves 0.891 AUC with only 3.8% performance degradation during crises, while providing early warnings 15 days before failures. 2026 IEEE. Subject The topic of the resource Dynamic Embeddings; Explanatory AI; Graph Neural Networks in Finance; Multi-Scale Learning; Systemic Risk; Transfer Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154970-1; 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