Parallel and Adaptive Graph Growth Methods for Shortest Path Computation in Big Data

Shortest path computation is a critical task in domains such as transportation, communication, and social networks, but conventional algorithms often struggle with the complexity and dynamism of big data environments. This study explores the use of graph growth algorithms to enhance shortest route calculations by incrementally expanding and adapting graph structures. The proposed framework emphasizes incremental construction, dynamic updating, and parallel processing to minimize computational overhead while maintaining adaptability to evolving datasets. Real-world applicability is demonstrated through models including random growth, preferential attachment, and community-based clustering, which collectively enhance scalability, fault tolerance, and system reliability. By integrating graph growth methods into shortest path analysis, the approach offers improved efficiency and responsiveness for large-scale applications such as logistics, healthcare, and finance. Quantitative evaluation confirms that graph growth techniques reduce computational overhead, maintain scalability across networks of increasing size, and achieve measurable improvements in pathfinding accuracy and processing efficiency, while still requiring significant resources in highly dynamic contexts.