Graph Neural Network-Driven Knowledge Discovery and Recommendation in Large-Scale Heterogeneous Information Networks
Keywords:
graph neural networks, heterogeneous information networks, knowledge discovery, recommendation systems, scalability, fairness, governance, large-scale systemsAbstract
The proliferation of large-scale heterogeneous information networks (HINs) across domains such as e-commerce, social media, bioinformatics, and scholarly communication has created an urgent need for advanced methods that can extract latent knowledge and deliver personalized recommendations from multi-typed, multi-relational data. Traditional recommendation algorithms and knowledge discovery techniques often fall short in capturing the complex structural and semantic dependencies inherent in HINs. Graph neural networks (GNNs) have emerged as a powerful paradigm that integrates network topology with node and edge features through iterative message passing and neighborhood aggregation, enabling end-to-end representation learning in heterogeneous spaces. This paper presents a comprehensive framework for GNN-driven knowledge discovery and recommendation within large-scale HINs, emphasizing system-level architectural design, computational trade-offs, deployment scalability, governance considerations, and fairness constraints. We survey foundational GNN variants adapted for heterogeneity, including relational graph convolutional networks, heterogeneous graph transformers, and metapath-guided aggregators, and analyze their respective strengths in capturing multi-hop semantics. The discussion extends to distributed training strategies, online inference pipelines, and memory-efficient sampling techniques required for industrial-scale graphs with billions of nodes and edges. Critical governance challenges such as algorithmic bias amplification through heterophilic links, transparency of learned embeddings, and sustainability of energy-intensive model training are examined through a socio-technical lens. We illustrate cross-domain deployment scenarios in content recommendation, drug discovery, and citation network analysis to highlight architectural invariants and domain-specific adaptations. The paper concludes with forward-looking perspectives on self-supervised learning for heterogeneous graphs, federated privacy-preserving GNNs, and regulatory alignment of automated recommendation systems. This work provides a systematic reference for researchers and practitioners aiming to operationalize GNNs in large-scale heterogeneous knowledge discovery and recommendation infrastructures.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
