Graph Theory Applications In Artificial Intelligence And Data Science

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Graph Theory has been established as a fundamental approach to modeling relational data in Artificial Intelligence and Data Science. In this paper, a thorough study of graph-related methods has been conducted with a special focus on Graph Neural Networks (GNNs) and its variants for processing non-Euclidean data structures. A new hybrid framework combining Graph Convolutional Network with attention model and hyperbolic embedding has been developed to overcome the drawbacks of current techniques. The proposed methodology is tested on standard datasets for the task of node classification and link prediction. It was found that the suggested technique provides better results than the baselines with an accuracy gain from 3% to 5%. The findings confirm the efficiency of the graph theoretical approach in dealing with complex dependencies and hierarchical relationships embedded in the data.