摘要当前，针对知识网络的链路预测主要是基于网络拓扑结构的相似性，很少考虑作者的研究领域，导致信息利用不充分等问题，因此本文提出了双层知识网络的链路预测框架hypernet2vec。双层知识网络，即作者合著关系网络和学术领域关系网络，利用网络表示学习，分别将两层网络中的节点映射到低维的向量空间，再输入到专门设计的卷积神经网络中计算并进行链路预测。与经典的链路预测指标如RA指标、LP指标和LRW指标等相比，hypernet2vec模型预测的AUC（area under curve）值取得了显著的提升，平均提升幅度达11.17%。文章还从情报产生层面和复杂系统层面，对模型发生作用的深层机理进行了探讨。
曹志鹏, 潘定, 潘启亮. 基于表示学习的双层知识网络链路预测[J]. 情报学报, 2021, 40(2): 135-144.
Cao Zhipeng, Pan Ding, Pan Qiliang. Link Prediction in Two-layer Knowledge Network Based on Network Representation Learning. 情报学报, 2021, 40(2): 135-144.
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