Graph tree fusion model for efficient real-time data processing

Abstract An innovative hybrid data model that integrates hierarchical tree structures with network-based graph representations to effectively manage dynamic, time-sensitive information. Developed using Python libraries for network analysis and discrete-event simulation, the model were evaluated using publicly available academic and social network datasets. It demonstrates efficient data traversal with logarithmic time complexity and minimal update overhead through techniques. Experimental analysis reveals significant improvements in update latency, memory efficiency, and system adaptability when compared to traditional data structures like graph-based trees. Statistical methods, including hypothesis testing and confirm the model's effectiveness for applications requiring real-time data processing.