Received 10.01.2025, Revised 20.03.2025, Accepted 24.04.2025

Artificial intelligence techniques for real-time visualisation of big data graph models

Andrii Banyk, Pavlo Mulesa

The purpose of the study was to develop approaches to the use of artificial intelligence to improve the processes of interactive visualisation of graph structures of big data in real time, considering the optimisation of computing resources. During the study, graphs were constructed for analysing relationships in big data, and computational intelligence methods were used to optimise the processing and visualisation of graphs in an interactive format. The results of the study included the development of programmes for building graph structures in Python in the Visual Studio Code environment and their further visualisation in Unity using C# in Visual Studio. First, a visualisation of a random Erdos-Renyi-type graph was shown, which was then recreated in Unity 3D space. Using Python libraries, graph generation and interactive web visualisation were implemented. Machine learning methods were used to optimise the location of nodes in graphs, in particular, autoencoders and principal components to reduce dimension. A demonstration of the Barbashi-Albert model allowed seeing the clustering of nodes and their relationships in real time. In addition, interactive visualisation was demonstrated, where nodes were located in 2D space according to the results of the principal components analysis. The use of the Louvain algorithm helped to perform clustering and visualise the structure of communities. The results showed that the use of neural networks significantly improves the accuracy and efficiency of node placement in graphs, and reduces computational complexity. The results obtained can be useful for scientific research involving the analysis of large graph structures and requiring interactive data visualisation

machine learning; interactive format; clustering and principal components; Python libraries; Unity capabilities
42-54
Banyk, A., & Mulesa, P. (2025). Artificial intelligence techniques for real-time visualisation of big data graph models. Information Technologies and Computer Engineering, 22(1), 42-54. https://doi.org/10.63341/vitce/1.2025.42

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