青海师范大学 a. 藏语智能信息处理及应用国家重点实验室;b.高原科学与可持续发展研究院,西宁 810008
A Multi-attribute Decision-making Method Based on Entropy to Identify Important Nodes in Hypernetworks
WU Yinghan, LI Mingda, HU Feng
a. The State Key Laboratory of Tibetan Intelligent Information Processing and Application; b. Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810008, China
Abstract：In order to overcome the deficiency of incomplete importance of nodes evaluated by single attribute and subjective weight selection of indicators, based on the K-shell method in hypernetwork, this paper introduces the influence of neighbor nodes on their own nodes while comprehensively considering the attributes of nodes, combined with the index of betweenness centrality, using the entropy method to determine the contribution weight of each index to node importance. A method to identify important nodes in hypernetworks is proposed from both local and global perspectives. The advantages and disadvantages of different identification methods are compared through the natural connectivity of network and the relative size of the maximum connected subgraph, and the empirical data of Xining city bus hypernetwork is used to further verify the effectiveness a feasibility of the proposed method.
吴英晗, 李明达, 胡枫. 基于熵的多属性决策超网络重要节点识别方法[J]. 复杂系统与复杂性科学, 2023, 20(4): 40-46.
WU Yinghan, LI Mingda, HU Feng. A Multi-attribute Decision-making Method Based on Entropy to Identify Important Nodes in Hypernetworks. Complex Systems and Complexity Science, 2023, 20(4): 40-46.
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