Machine Learning Methods with Link-Level Features

Author(s)	Koffka Khan
Country	Trinidad and Tobago
Abstract	This paper describes graph link level features. We describe three different kinds of them. We discuss distance-based characteristics that consumers might use, such as the shortest path between two nodes, which does not account for neighborhood overlaps. Then we discuss neighborhood overlap metrics like Common neighbors, Jaccard coefficient, and the Adamic-Adar index that measures how many neighbors a pair of nodes share in a fine-grained manner. The issue with this is that the metric will return a value of 0 for nodes that are more than two hops away or that do not have any neighbors. The global neighborhood overlap metrics, for instance, like Katz use the global graph structure to give us a score for a pair of nodes, and the Katz index counts the number of pets of all lands between a pair of nodes where these paths are discounted exponentially with their length.
Keywords	graph, link, features, common neighbors, Jaccard coefficient, Adamic-Adar index, Katz index
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-12-30
Cite This	Machine Learning Methods with Link-Level Features - Koffka Khan - IJFMR Volume 4, Issue 6, November-December 2022. DOI 10.36948/ijfmr.2022.v04i06.1281
DOI	https://doi.org/10.36948/ijfmr.2022.v04i06.1281
Short DOI	https://doi.org/grkbwf

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research