Work

Enhancing Algorithmic Techniques for Streamlined Complex Graph Structures in Big Data Visualization

Year: 2025

Type: article

Abstract: With the rapid expansion of data applications, particularly in large and complex graph structures, effective visualization and analysis tools are essential. This paper addresses the "Hair Ball" problem, where excessive node and edge intersections hinder the clear interpretation of networks. To mitigate this issue, an efficient algorithm based on the K3,4 bipartite graph model is proposed. The model is systematically compressed to reduce intersecting edges while preserving essential structural relationships. The algorithm was tested on various datasets, ranging from small synthetic networks to large real-world graphs. The results demonstrate significant reductions in visual complexity and improved clarity. Key performance metrics, including edge density reduction and observer feedback, validate the scalability and practical applicability of the proposed approach in big data environments. By simplifying intricate graph structures, this method offers a versatile and effective solution for applications in network analysis, data visualization, and related fields.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.9740

Author: Khalid Hamad Alnafisah

Institution: Northern Border University

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $431

Funder: Northern Border University