Logistic regression based DFS for Trip Advising Software (ASCEND)

Abstract

Graphs have played a pivotal role in the field of computer science and has been an efficient method for representing and modeling abstractions in various fields. They can be used to represent several real life models. Several domains in today's world use the concept of graphs extensively such as GPS Navigation systems, Computer networks, WebCrawler, Social Networking websites, peer to peer networking, medical and biological field, neural networks etc. Taking into account the numerous applications of the concept of graphs in today's world, graph searching becomes inevitably significant. In this scenario it is important to note that several graph searching algorithms that were proposed to give exhaustive searches doesn't provide the most satisfying outcome in terms of asymptotic time complexity. Through this paper we intend to highlight the significance of machine learning as a useful tool that can be incorporated in various graph searching algorithms that can reduce its complexity. We classify the existing graph searching techniques as subsets or modifications of two major conventional graph searching algorithms namely BFS(Breadth First Search) and DFS(Depth First Search) and suggest the application of logistic regression to improve their performance. It is confounding that only few research papers explore the application of machine learning to the aforementioned graph searching algorithms. Hence, it is evident that there exists scope for future research on this topic and we aim to suggest directions for the same. � 2019 IEEE.