Strength of Connections in a Random Graph: Definition, Characterization, and Estimation
Statistics Theory
2015-12-11 v3 Methodology
Statistics Theory
Abstract
How can the `affinity' or `strength' of ties of a random graph be characterized and compactly represented? How can concepts like Fourier and inverse-Fourier like transform be developed for graph data? To do so, we introduce a new graph-theoretic function called `Graph Correlation Density Field' (or in short GraField), which differs from the traditional edge probability density-based approaches, to completely characterize tie-strength between graph nodes. Our approach further allows frequency domain analysis, applicable for both directed and undirected random graphs.
Keywords
Cite
@article{arxiv.1412.1530,
title = {Strength of Connections in a Random Graph: Definition, Characterization, and Estimation},
author = {Subhadeep Mukhopadhyay},
journal= {arXiv preprint arXiv:1412.1530},
year = {2015}
}
Comments
18 pages, 6 Figures. Third version