International Journal For Multidisciplinary Research

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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

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

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