A multi-core periphery perspective: Ranking via relative centrality
Chandra Sekhar Mukherjee, Jiapeng Zhang

TL;DR
This paper introduces a novel relative centrality measure for detecting core-periphery structures in graphs, improving core detection accuracy and community separation, with applications to biological datasets.
Contribution
It proposes a new relative centrality concept that reduces bias in core detection algorithms and enhances community detection in graphs with core-periphery structures.
Findings
Relative centrality reduces bias compared to PageRank and degree centrality.
The method improves community detection and vertex selection in biological datasets.
Experiments show better clustering performance using the proposed approach.
Abstract
Community and core-periphery are two widely studied graph structures, with their coexistence observed in real-world graphs (Rombach, Porter, Fowler \& Mucha [SIAM J. App. Math. 2014, SIAM Review 2017]). However, the nature of this coexistence is not well understood and has been pointed out as an open problem (Yanchenko \& Sengupta [Statistics Surveys, 2023]). Especially, the impact of inferring the core-periphery structure of a graph on understanding its community structure is not well utilized. In this direction, we introduce a novel quantification for graphs with ground truth communities, where each community has a densely connected part (the core), and the rest is more sparse (the periphery), with inter-community edges more frequent between the peripheries. Built on this structure, we propose a new algorithmic concept that we call relative centrality to detect the cores. We observe…
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Taxonomy
TopicsGlobal Trade and Competitiveness
