Abstract: With the in-depth development of complex network research, using complex network theory to model and analyze traditional Chinese medicine prescription data has become an important method for studying the compatibility of traditional Chinese medicine. However, the traditional “medicine-medicine” network based on the “node-edge” structure cannot reflect the higher-order interaction between prescriptions and Chinese herbs, which affects the accuracy and effectiveness of medicine pair mining. To address this issue, this paper proposes a hypergraph-based method for modeling the prescription hyper-network to capture higher-order correlations between prescriptions and Chinese herbs. The prescription hyper-network is constructed by treating prescriptions as hyper-edges and Chinese herbs in prescriptions as nodes. By comparing the statistical properties of the traditional “medicine-medicine” network and the prescription hyper-network, it is found that the prescription hyper-network has a node hyper-degree distribution that conforms more closely to power-law distribution characteristics and exhibits small-world and scale-free properties. This proves the rationality and feasibility of the prescription hyper-network modeling method. Based on the hyper-network of traditional Chinese medicine formulae, a double weight degree centrality (DWDC) index is proposed to identify commonly used herbs in formulae. The impact of hyperedges on node characteristics is considered, and the fragility and robustness of the network are used as a basis. Through comparative experiments with various algorithms, it is demonstrated that the proposed method can more effectively screen out commonly used herbs in the hyper-network of formulae. The DWDC index is further extended to identify commonly used herb pairs in the second order, and a higher-order index H-DWDC is proposed. Experimental results based on a real dataset show that this method can effectively extract commonly used herb pairs from massive data.

Key words: complex network, prescription hyper-network, double weight degree centrality (DWDC), medicine pair mining, high-order double weight degree centrality (H-DWDC)

摘要： 随着复杂网络研究的深入发展，利用复杂网络理论对中药药方数据进行建模分析已成为研究中药配伍规律的重要方法之一。然而，传统的“药-药”网络结构只基于“点-边”关系，无法体现方剂和中药之间的高阶交互关系，从而影响药对挖掘的准确率和效果。针对该问题，提出了一种基于超图的方药超网络建模方法来刻画方剂与中药之间的高阶关联关系，以方剂为超边、方剂中的中药为节点构建方药超网络，对比传统“药-药”网络和方药超网络的统计特性发现，方药超网络节点超度分布更加符合幂律分布特征，具有小世界和无标度特性，证明了方药超网络建模方法具有合理性和可行性；基于方药超网络提出一种融合多因素的双权重度中心性指标（double weight degree centrality，DWDC）挖掘方剂中的常见中药，引入超边属性对节点特性的影响，以网络的脆弱性和鲁棒性为依据，通过各算法之间的对比实验，验证所提出方法能够更有效筛选出方剂超网络中的常见中药；再将DWDC指标推广到二阶常见药对挖掘，提出高阶指标H-DWDC，基于真实数据集的实验结果表明，该方法能够有效地从海量数据中挖掘出常见药对。

关键词: 复杂网络, 方药超网络, 双权重度中心性（DWDC）, 药对挖掘, 高阶双权重度中心性（H-DWDC）