A BRIEF SURVEY ON DISTRIBUTED GRAPH ALGORITHMS FOR SHORTEST DISTANCE

There is an extended history of study in theoretical computer science faithful to designing proficient algorithms for graph problems. In several modern applications the graph in query is altering over time, and to avoid rerunning algorithm on the entire graph every time a small change occurs. This paper aims to present a brief survey on graph theory based on Shortest Distances in Dynamic Graphs techniques in which the goal is to minimize the amount of work needed to re-optimize the solution when the graph changes. Number of relative studies namely Graph pattern matching, Spatially Induced Linkage Cognizance (SILC), Snowball Algorithm, GREEDY-SNDOP, APSP and Efficient incremental algorithms are discussed and evaluate the running time performance on the several datasets. Comparing to these algorithms the efficient incremental algorithm techniques methods outperforms having better performance than other methods.