Towards shortest path identification on large networks
The use of Big Data in today’s world has become a necessity due to the massive number of technologies developed recently that keeps on providing us with data such as sensors, surveillance system and even smart phones and smart wearable devices they all tend to produce a lot of information that need to be analyzed and studied in details to provide us with some insight to what these data represent. In this paper we focus on the application of the techniques of data reduction based on data nodes in large networks datasets by computing data similarity computation, maximum similarity clique (MSC) and then finding the shortest path in a quick manner due to the data reduction in the graph. As the number of vertices and edges tend to increase on large networks the aim of this article is to make the reduction of the network that will cause an impact on calculating the shortest path for a faster analysis in a shortest time. © 2016, The Author(s).
Towards shortest path identification on large networks.
Journal of Big Data, 3(1),