Heuristic Based Ex-FTCD Algorithm for Incremental Path Finding in Dynamic Graphs

The data when modeled as a graph can bring out a lot of useful information. Path finding is one of the most popular applications of network analysis. As the data evolution speed from various sources has grown tremendously, the need for incremental mining is on high demand. Algorithms that compute the shortest path on dynamically changing data needs to be designed. In this paper, we propose a fully dynamic scalable and parallelizable method for finding the all pair shortest path. The work includes computation of the dynamic shortest on network modifications like edge insertion, edge deletion and weight updation, and eliminates recomputing from the scratch. The concept of exact heuristics is applied in the results obtained from the actual network. Validity and efficiency of the proposed method is proved using synthetically generated as well as real world networks.