Kirill Kovalenko, Miguel Romance, Ekaterina Vasilyeva, David Aleja, Regino Criado, Daniil Musatov, Andrei M. Raigorodskii, Julio Flores, Ivan Samoylenko, Karin … 展开作者
Kirill Kovalenko, Miguel Romance, Ekaterina Vasilyeva, David Aleja, Regino Criado, Daniil Musatov, Andrei M. Raigorodskii, Julio Flores, Ivan Samoylenko, Karin Alfaro-Bittner, Matjaz Perc, Stefano Boccaletti
Identifying the most influential nodes in networked systems is of vital importance to optimize their function and control. Several scalar metrics have been proposed to that effect, but the recent shift in focus towards network structures which go beyond a simple collection of dyadic interactions has rendered them void of performance guarantees. We here introduce a new measure of node's centrality, which is no longer a scalar value, but a vector with dimension one lower than the highest order of interaction in a hypergraph. Such a vectorial measure is linked to the eigenvector centrality for networks containing only dyadic interactions, but it has a significant added value in all other situations where interactions occur at higher-orders. In particular, it is able to unveil different roles which may be played by the same node at different orders of interactions -- information that is otherwise impossible to retrieve by single scalar measures. We demonstrate the efficacy of our measure with applications to synthetic networks and to three real world hypergraphs, and compare our results with those obtained by applying other scalar measures of centrality proposed in the literature.
A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights
Kim Jaechul Graduate School of AI, KAIST（KAIST 金在澈人工智能研究生院）；School of Electrical Engineering, KAIST（KAIST 电气工程学院）；Intesa Sanpaolo（圣保罗银行）；Intesa Sanpaolo AI Research（圣保罗银行AI研究院）
P. N. Lebedev Physical Institute of the Russian Academy of Sciences（俄罗斯科学院列别杰夫物理研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；ITMO University（ITMO大学）；Caucasus Mathematical Center, Adyghe State University（阿迪亚格州立大学高加索数学中心）；Faculty of Mechanics and Mathematics, Moscow State University（莫斯科大学力学与数学系）；Institute of Mathematics and Computer Science, Buryat State University（布里亚特国立大学数学与计算机科学研究所）；Institute of Interdisciplinary Intelligent Science, Ningbo University of Technology（宁波工程学院跨学科智能科学研究所）；School of Mathematics, North University of China（中北大学数学学院）；Institute of Smart Agriculture for Safe and Functional Foods and Supplements, Trakia University（特拉基亚大学安全功能性食品与补充剂智能农业研究所）；CNR – Institute of Complex Systems（意大利国家研究委员会复杂系统研究所）
A unified framework for identifying influential nodes in hypergraphs
Beihang University（北京航空航天大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新学院）；Zhongguancun Laboratory（中关村实验室）；Institute of Trustworthy Artificial Intelligence, Zhejiang Normal University（浙江师范大学可信人工智能研究院）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
Higher-order rich clubs and configuration models on general directed hypergraphs
Nottingham Trent University（诺丁汉特伦特大学）；Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克科学计量学研究所）；Institute of Clinical and Preventive Medicine, University of Latvia（拉脱维亚大学临床与预防医学研究所）；Graz University of Technology（格拉茨工业大学）；Computer and Information Sciences, University of Strathclyde（思克莱德大学计算机与信息科学学院）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
University of Konstanz（康斯坦茨大学）；University of Bucharest（布加勒斯特大学）；The Center for Innovation in Medicine（医学创新中心）；University of Maribor（马里博尔大学）；Community Healthcare Center Dr. Adolf Drolc Maribor（马里博尔阿道夫·德罗尔博士社区健康中心）；Kyung Hee University（庆熙大学）；Korea University（韩国大学）
