Optimizing Minimum Spanning Tree Algorithm for Efficient Graph Processing in Big Data

Optimizing Minimum Spanning Tree (MST) techniques improve large data graph processing. Developing improved methods to manage large-scale data set computational complexity is the goal. To calculate MSTs in large networks faster and cheaper, better data structures and parallel processing are used. This entails improving algorithms and creating new ones for scalability and accuracy. The goal is to design a robust system that can handle enormous graphs quickly and reliably, improving big data analysis and decision-making. The focus is on algorithmic efficiency, resource optimization, and real-world big data application. Protein Structure Dataset shows the Runtime Efficiency of Optimized MST Algorithm in data processing with up to 1,000,000 data nodes, with Initial Runtime (seconds) values of 300-1600 and Optimized Runtime (seconds) values of 180-700. Memory Consumption Comparison shows the same dataset. In another instance of the same dataset from Enhanced Edge Processing, edge density ranges from 1 to 10 million, Traditional Speed (edges/second) from 5000-10000, and Optimized Speed from 10000-20000.