Towards shortest path identification on large networks

Abstract

IntroductionOver the past 10 years, there has been vast improvement in hardware architecture design for computer information, one of the most important functions being network analysis. The main problem with network analysis is the shortest path analysis. According to the network being analyzed, the shortest path has a variety of measurements, such as time, to find the path. The problem with determining the shortest path, however, is to find both the fastest and the shortest path. Thus, research in the shortest path always has been a point of interest in graph theory.Frequently, graphs are used along with modern technology in such setting as online social networks (e.g., LinkedIn ™ or Facebook). Since the size of the graphs is increasing exponentially, many direct processes become more demanding. For example, LinkedIna well-known website for professional networking-that tries to connect professionals together worldwide. If a person is trying to get in touch with someone from the human resource department in a company by using LinkedIn, what the website does is try to find the shortest path to reach that person in that specific company, starting from his connections and moving on to friends of friend to reach the desired personal in the specified company. Similarly, this application of the shortest path can be used over and over in different scenarios, for example, finding routes from one point to another point AbstractThe 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.

An Improved Simulation Based Method forSelection of Cell in Cellular Network

“…But if the user is doing a job in which he / she has to work at single place then in that situation the change in location as well as cell selection is comparatively less in numbers. Various models for Mobility patterns are given below (1) Memory less (Random Walk) Movement Model [18] (2) Markovian Movement Model [19] (3) Shortest Distance Model [20] (4) Gauss Markov Model [21] (5) Activity based model [22] (6) Mobility Trace [23] (7) Fluid-Flow Model [24] Call Arrival Pattern Time is the important factor to describe the Call Arrival Pattern or rate at which the user will receive the calls. User always gets more number of call during working hours as compared to non-working hours.…”

Routing Algorithm for Fastest Path – a Queuing Approach

“…Let be an adjacent node to ' which is not permanently labelled. The Average waiting time of a packet in the node is calculated by (2) The Average number of packets waiting in the node is given by (3) Probability that the number of packets exceeds ' at node is…”

Vaccination allocation in large dynamic networks

“…Through these financial support, the following articles have been published [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”