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Natarajan Meghanathan

Biographic Data

ID8546993
NAMENatarajan Meghanathan
GIVEN NAMESNatarajan
FAMILY NAMEMeghanathan
SIGNATUREMEGHANATHAN N
AFFILIATIONSJackson State University
ORCID0000-0001-8565-4086
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS0
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2017
LATEST PUBLICATION YEAR2025
H-INDEX0
  • Principal component analysis of link prediction scores to propose a binary classification model for the betweenness of edges in complex networks

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2025

    The problem of quantifying edge betweenness in complex networks is computationally-intensive. We propose a novel idea of rather simply classifying the edges as those of high-betweenness or low-betweenness. We make use of the tendency of neighborhood-based link prediction techniques to highly score node pairs (including those connected through edges) that are already part of a dense sub graph. We compute link prediction scores (using four differen…

  • Principal components-based quantification of hierarchical k-core assortativity

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2024

  • Randomness Index for complex network analysis

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2017

No prominent works on this page.

  • Randomness Index for complex network analysis

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2017

  • Principal components-based quantification of hierarchical k-core assortativity

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2024

  • Principal component analysis of link prediction scores to propose a binary classification model for the betweenness of edges in complex networks

    Open Access•Natarajan Meghanathan•ARTICLE•Social Network Analysis and Mining•2025

    The problem of quantifying edge betweenness in complex networks is computationally-intensive. We propose a novel idea of rather simply classifying the edges as those of high-betweenness or low-betweenness. We make use of the tendency of neighborhood-based link prediction techniques to highly score node pairs (including those connected through edges) that are already part of a dense sub graph. We compute link prediction scores (using four differen…

Artificial Intelligence (3 works) · Complex Network Analysis Techniques (3 works) · Computer Science (3 works) · Combinatorics (2 works) · Complex network (2 works) · Data mining (2 works) · Mathematics (2 works) · Opinion Dynamics and Social Influence (2 works) · Principal component analysis (2 works) · Advanced Clustering Algorithms Research (1 work)

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