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| Identification of Important Nodes in Temporal Networks Based on Weighted Penalty Local Structure Entropy |
| YU Lufena, SUN Wenjinga, PAN Wenlina, ZHANG Tianjuna, HU Zhitaoa, NIE Tengtaob
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| a. School of Mathematics and Computer Science; b. School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China |
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Abstract Focused on the issue that the connection mode and activity of nodes in the identification of important nodes in temporal networks are changed with time, and the complexity of the network structure around nodes is considered. In this paper, a new algorithm called Temporal Penalized Local structure Entropy Advancement (TPLEA) was proposed to identify important nodes. The algorithm was combined with the time window graph model and the Penalty Local Structure Entropy Advancement (PLEA) model, and introduced node activity weight and the contribution rate weight of node degree to determine the comprehensive weight of the node, and finally obtained the importance of each node. The effectiveness and applicability of the method were verified on six real datasets, and ablation experiments were carried out on the introduced weight factors. The experimental results show that this method can effectively identify the important nodes in the temporal network, and the comprehensive weight factors of the nodes have a great influence on the recognition effect of this method.
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Received: 30 January 2024
Published: 19 May 2026
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