Elaris Computing Nexus
Knowledge Guided Graph Neural Computational Model for Context Aware Dependency Reasoning in Complex Information Networks
Elaris Computing Nexus
Received On : 06 December 2025
Revised On : 16 February 2026
Accepted On : 23 February 2026
Published On : 28 February 2026
Volume 02, 2026
Pages : 038-052
Abstract
Complex information networks possess intricate structural dependencies and heterogeneous semantic relationships, where accurate context-aware reasoning is challenging for conventional graph learning models. Current Graph Neural Networks (GNNs) focus mainly on aggregating information from the immediate neighborhood and tend to lack the incorporation of domain knowledge and dynamic contextual dependencies, leading to sub-optimal reasoning and knowledge transfer in dense and dynamic graph environments. In order to solve these limitations, this paper presents a novel computational framework, named as KG-CDRNet (Knowledge-Guided Context-aware Dependency Reasoning Network), which combines knowledge-guided representation learning and adaptive context-aware dependency reasoning for complex information networks. The proposed framework builds its node representations by utilizing knowledge-guided feature initialization and then gradually learns to propagate the informative dependencies by a hierarchical context-aware graph attention mechanism, while discarding irrelevant interactions and noise. In addition, a dynamic dependency reasoning module is proposed to enhance the semantic relationships by iteratively propagating knowledge and optimizing them with dependency awareness, which further promotes the consistency and interpretability of graph representations. The overall model is modelled as a single optimization problem where the preserving of structural topology, contextual semantics and knowledge consistency occur simultaneously in the graph learning process. The results of the extensive experiments conducted on benchmark information network datasets confirm that KG-CDRNet consistently outperforms the existing state of the art graph learning approaches over multiple different evaluation metrics such as Accuracy, Precision, Recall, F1-score and Area Under the Receiver Operating Characteristic Curve (AUC). Proposed framework outperforms competitive baseline models with an average classification accuracy of 98.74%, a 21.36% decrease in the complexity of dependency reasoning, and a 18.92% increase in the quality of the contextual representation. The results are consistent, confirm the effectiveness, robustness, and scalability of KG-CDRNet in dependency reasoning in complex information networks, while keeping the context information.
Keywords
Knowledge-Guided Graph Neural Network (KG-GNN), Context-Aware Dependency Reasoning, Knowledge Graph Embedding, Graph Attention Networks, Complex Information Networks.
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Yang Gao, “Knowledge Guided Graph Neural Computational Model for Context Aware Dependency Reasoning in Complex Information Networks”, Elaris Computing Nexus, pp. 038-052, 2026, doi: 10.65148/ECN/2026004.
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© 2026 Yang Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.