Node ranking based on graph curvature and PageRank  

作　　者：Hongbo Qu Yu-Rong Song Ruqi Li Min Li Guo-Ping Jiang 曲鸿博;宋玉蓉;李汝琦;李敏;蒋国平

机构地区：[1]School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China [2]College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

出　　处：《Chinese Physics B》2025年第2期496-507,共12页中国物理B(英文版)

基　　金：Project partially supported by the National Natural Science Foundation of China (Grant Nos. 61672298 and 62373197);the Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province,China (Grant No. 2018SJZDI142);the Postgraduate Research & Practice Innovation Program of Jiangsu Province,China (Grant No. KYCX23 1045)。

摘　　要：Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these networks. The Page Rank algorithm, widely recognized for ranking web pages, offers a more nuanced approach by considering the importance of connected nodes. However, existing methods generally overlook the geometric properties of networks, which can provide additional insights into their structure and functionality. In this paper, we propose a novel method named Curv-Page Rank(C-PR), which integrates network curvature and Page Rank to identify influential nodes in complex networks. By leveraging the geometric insights provided by curvature alongside structural properties, C-PR offers a more comprehensive measure of a node's influence. Our approach is particularly effective in networks with community structures, where it excels at pinpointing bridge nodes critical for maintaining connectivity and facilitating information flow. We validate the effectiveness of C-PR through extensive experiments. The results demonstrate that C-PR outperforms traditional centrality-based and Page Rank methods in identifying critical nodes. Our findings offer fresh insights into the structural importance of nodes across diverse network configurations, highlighting the potential of incorporating geometric properties into network analysis.

关 键 词：important nodes graph curvature complex networks network geometry 

分 类 号：O157.5[理学—数学]