摘要
将K-shell指标扩展到超网络中,避免了超网络中超度较大、但位于超网络边缘位置的节点对挖掘关键节点带来的影响。由于K-shell方法的局限性,导致节点排序结果过于粗糙。针对这一问题,结合超度和K-shell(ks)值利用欧式距离公式提出识别超网络关键节点的k^(d)_(s)指标,并利用蛋白复合物超网络进行验证。实验证明,k^(d)_(s)指标能够准确有效地识别超网络中的关键节点。
In this paper,the K-shell index is extended to the hypernetwork to avoid the influence of the nodes with larger hyperdegree but located at the edge of the hypernetwork on the mining of vital nodes.Due to the limitation of K-shell method,the result of node sorting is too rough.In order to solve this problem,this paper proposes a k^(d)_(s)(complex K-shell and degree)index to identify the vital nodes of the hypernetwork by combining the hyperdegree and K-shell(k s)value and using the Euclidean distance formula,and verifies it by using the protein complex hypernetwork.Experiments show that k^(d)_(s)index can accurately and effectively identify the vital nodes in the hypernetwork.
作者
周丽娜
李发旭
巩云超
胡枫
ZHOU Lina;LI Faxu;GONG Yunchao;HU Feng(Computer College,Qinghai Normal University,Xining 810008,China;Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province,Qinghai Normal University,Xining 810008,China;The State Key Laboratory of Tibetan Intelligent Information Processing and Application,Qinghai Normal University,Xining 810008,China)
出处
《复杂系统与复杂性科学》
CAS
CSCD
北大核心
2021年第3期15-22,共8页
Complex Systems and Complexity Science
基金
国家自然科学基金(61663041)
青海科技计划项目(2018ZJ718)。
