An energy-aware diffusion graph neural network based on intuitionistic fuzzy credibility graphs for research-paper classification

The classification of research papers from different academic data is still challenging due to the presence of semantic ambiguity, lack of information, fluctuating credibility, and complex linkages across research articles. Existing fuzzy graph neural networks depend too much on membership information and cannot truly consider the intuitionistic uncertainty, trust dependability, and structural importance of nodes at the graph propagation level. To overcome these constraints, this paper presents an intuitionistic fuzzy graph neural network architecture that combines trust-aware intuitionistic representations and a unique node Laplacian energy mechanism. The suggested Node Laplacian Energy is derived from the graph Laplacian's row-wise norm and is used as an adaptive node-weighting metric to assess each research paper's local structural contribution. Furthermore, a diffusion-based propagation technique is used to enhance information transmission across semantically linked publications and capture larger graph relationships. The suggested system is tested for research-paper categorization with semantic similarity features and intuitionistic fuzzy credibility information. Its performance is compared to known machine learning and graph neural network baselines utilizing accuracy, precision, recall, and F1-score. The findings show that including uncertainty-aware representations, structural node weighting, and diffusion-based graph propagation can improve discriminative academic content categorization. The suggested paradigm offers a unified way to model semantic linkages, uncertainty, believability, and graph structural relevance in research-paper categorization.