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| A Method for Calculating Node Similarity in Hypernetworks Based on Higher-order Distance Distribution |
| YANG Yusheng, GUO Lei, FAN Jingyan, HU Feng
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| a. College of Computer; b. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China |
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Abstract To address the limitations of traditional hypernetwork node similarity methods in capturing higher-order topological structures, a method for calculating node similarity in hypernetworks based on Higher-order Distance Distribution (HDDNS) is proposed. The method generates higher-order distance distributions for nodes and employs Jensen-Shannon divergence to quantify inter-node similarity. To evaluate its effectiveness, this study conducted systematic experiments on three synthetic hypernetworks and five real-world hypernetworks, comparative analysis with five existing methods demonstrates that HDDNS exhibits superior performance in two key evaluation dimensions: mutual similarity metrics and propagation influence, the results indicate that HDDNS enables more precise and efficient computation of inter-node similarity.
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Received: 22 October 2024
Published: 14 July 2026
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