[目的/意义] 通过构建二模复杂网络模型，揭示隐藏在海量文献中的隐性知识。[方法/过程] 通过NetworkX复杂网络工具包，依据任意两个节点的共现关系构建二模复杂网络模型；对网络模型中节点的共现关系进行加权，计算网络的拓扑信息并进行AP聚类，提取节点间的直接关系；采用AUC方法对AA、JC、加权改进的wAA和wJC等4种链路预测算法进行评价，遴选出最合适的预测算法，并对复杂网络的隐性关系进行预测分析。[结果/结论] 以潜在药物靶点挖掘为例进行的实证研究结果表明，wAA链路预测算法为最优的链路预测算法；二模复杂网络模型、指标和方法体系在美国化学文摘社数据库中的药物靶点挖掘中具有一定的有效性。下一步计划在其他数据库中或其他研究领域中进行尝试，以进一步验证该模型的通用性和有效性。
[Purpose/significance] This paper aims to extract the tacit knowledge from the massive literatures by constructing a two-mode complex network model. [Method/process] Through the NetworkX complex network toolkit, a two-mode complex network model was constructed based on the co-occurrence relationship of any two nodes. The direct relationship between nodes and nodes was extracted by weighting the co-occurrence relationship of nodes in the network model, calculating the topology information of the network and AP clustering. The most appropriate prediction algorithm was selected by using AUC method to evaluate the four link prediction algorithms, such as AA, JC, wAA and wJC. The tacit knowledge was predicted by the most appropriate prediction algorithm from the complex networks. [Result/conclusion] The results showed that the wAA link prediction algorithm was the optimal link prediction algorithm. The two mode complex network model, indicators and method system were effective in drug target mining in the Chemical Abstracts Service database. The next step is to try in other databases or other research fields to further verify the generality and effectiveness of the model.
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