2014/12/04 by Subhadeep Mukhopadhyay, Mukhopadhyay, Subhadeep · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #Graph theory and applications #Methodology (stat.ME) #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.1412.1530
18 pages, 6 Figures. Third version
openalex publication_date 2014/12/04 · arxiv created 2015/12/09 · arxiv updated 2015/12/11 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.