Graph Foundation Models for Cross-Domain Knowledge Integration and Analytics

  • Authors

    • Seppo Linnainmaa Professor of Computer Science, University of Helsinki, Finland. Author
    • Arto Salomaa Professor of Computer Science, University of Turku, Finland. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2025PI4K2E

    Published 03-05-2025

  • Graph Foundation Models, Graph Neural Networks, Knowledge Graphs, Cross-Domain Knowledge Integration, Representation Learning, Graph Transformers, Artificial Intelligence, Self-Supervised Learning, Knowledge Analytics, Graph Embedding

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    S. Linnainmaa and A. Salomaa, “Graph Foundation Models for Cross-Domain Knowledge Integration and Analytics”, IJETMR, vol. 8, no. 1, pp. 01–15, Mar. 2025, doi: 10.67228/30715636/IJETMR-2025PI4K2E.

  • Abstract

    Graph Foundation Models (GFMs) enable universal representation learning from heterogeneous graph-structured data through large-scale self-supervised pretraining. Unlike traditional Graph Neural Networks (GNNs), GFMs learn transferable structural, semantic, and contextual knowledge across multiple domains, including healthcare, finance, manufacturing, cybersecurity, and smart cities, making them highly effective for cross-domain knowledge integration. This paper presents an intelligent multi-layer GFM framework for integrating heterogeneous knowledge graphs and supporting scalable cross-domain analytics. The framework combines graph representation learning, knowledge graph embedding, transformer-based graph encoders, self-supervised contrastive learning, and domain adaptation to perform semantic alignment, feature extraction, graph embedding optimization, and downstream reasoning within a unified environment. A cross-domain integration pipeline automatically aligns entities, relationships, semantics, and graph topologies from diverse data sources while transformer-based graph attention captures both local and global structural dependencies. The proposed framework is evaluated using metrics such as knowledge integration accuracy, graph embedding accuracy, node classification, link prediction, semantic consistency, computational efficiency, scalability, and inference latency. Experimental results demonstrate improved cross-domain representation learning, enhanced transfer learning, reduced feature engineering, and lower dependence on labeled data compared with conventional graph learning approaches. The framework provides a scalable foundation for graph-based artificial intelligence and has applications in biomedical knowledge discovery, financial fraud detection, industrial digital twins, recommendation systems, cybersecurity intelligence, scientific literature mining, and smart governance. Overall, Graph Foundation Models offer a promising solution for universal graph intelligence, enabling accurate cross-domain reasoning, predictive analytics, and explainable decision-making.

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