Network alignment based on multiple hypernetwork attributes

作者全名："Peng, Hao; Li, Shuzhe; Zhao, Dandan; Zhong, Ming; Qian, Cheng; Wang, Wei"

作者地址："[Peng, Hao] Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zhejia, Yingbin Ave, Jinhua 321004, Zhejiang, Peoples R China; [Peng, Hao; Li, Shuzhe; Zhao, Dandan; Zhong, Ming; Qian, Cheng] Zhejiang Normal Univ, Sch Comp Sci & Technol, Yingbin Ave, Jinhua 321004, Zhejiang, Peoples R China; [Wang, Wei] Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China"

通信作者："Wang, W (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China."

来源：EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS

ESI学科分类：PHYSICS

WOS号：WOS:001183600900001

JCR分区：Q2

影响因子：2.6

年份：2024

卷号：　

期号：　

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文献类型：Article; Early Access

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摘要："The network alignment problem refers to how to find the node correspondence across different networks in multiplex networks. This study has significant implications in various disciplinary fields. However, current network alignment work focuses on simple networks. These methods based on simple networks are doomed to fail to capture high-order relationships. In order to fill the gap in this area, this paper will introduce a prediction method of inter-layer connectivity based on multi hypernetwork structure attributes. Among them, the hyperedge similarity index of nodes is specially designed for higher-order relationships in hypernetworks, and a degree punishment mechanism is designed to reasonably evaluate the similarity of higher-order relationships between nodes in different environments. This method further considers the quantity and strength information of the similarity of higher-order relations, which helps to further increase the accuracy rate on the hypernetwork. We compare this method with other advanced methods on different real-world hypernetworks and artificial hypernetworks. Experiments show that the method has good performance and robustness. In the biological metabolic network, the accuracy of this method can even be improved by 29.8% compared with the comparison method. In the 38 groups of data we tested, the accuracy rate was 78.9% when it was stably higher than other methods, 15.9% when it was not lower than the comparison method, and only 5.2% when the method performed poorly."

基金机构："the National Natural Science Foundation of China [LD24F020002]; Natural Science Foundation of Zhejiang Province, China [62072412, 61902359, 61702148, 61672468]; National Natural Science Foundation of China [AGK-2018001]; Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security [C20607]; Key Lab of Information Network Security, China, Ministry of Public Security, China [cstc2021jcyj-msxmX0132]; Natural Science Foundation of Chongqing, China [W0150]; Program for Youth Innovation in Future Medicine, Chongqing Medical University, China"

基金资助正文："This work was supported by the Natural Science Foundation of Zhejiang Province, China, under No. LD24F020002 and partly supported by the National Natural Science Foundation of China under grant nos. 62072412, 61902359, 61702148, and 61672468, the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security under grant no. AGK-2018001, the Key Lab of Information Network Security, China, Ministry of Public Security, China, under grant no. C20607 and the Natural Science Foundation of Chongqing, China, No. cstc2021jcyj-msxmX0132. Program for Youth Innovation in Future Medicine, Chongqing Medical University, China, No. W0150."