| 基于表示学习的双层知识网络链路预测 |
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| 引用本文： | 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(2):135-144. |
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| 作者姓名： | 曹志鹏 潘定 潘启亮 |
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| 作者单位： | 暨南大学 |
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| 基金项目： | 广东省社科规划项目“广东省高校知识网络的结构和演化机制研究”(GD17YGL04)。 |
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| 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。
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| 关 键 词： | 知识网络 链路预测 神经网络 表示学习 |
Link Prediction in Two-layer Knowledge Network Based on Network Representation Learning |
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| Cao Zhipeng,Pan Ding,Pan Qiliang.Link Prediction in Two-layer Knowledge Network Based on Network Representation Learning[J].Journal of the China Society for Scientific andTechnical Information,2021(2):135-144. |
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| Authors: | Cao Zhipeng Pan Ding Pan Qiliang |
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| Institution: | (Jinan University,Guangzhou 510632) |
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| Abstract: | In recent years, most link prediction algorithms have focused on the similarity of the knowledge network.s topological characteristics, with less consideration of the author.s research field, which lead to some problems, such as insufficient information utilization. This paper proposes hypernet2 vec model, a link prediction model for a two-layer knowledge network. The two-layer knowledge network consists of the Co-author Network and Academic Field Relationship Network.The nodes in the two-layer network are mapped to a low-dimensional vector space by network representation learning, and then they are fed into a convolutional neural network, which is specially designed to calculate and predict future links. The empirical results of the evaluation on real-world networks demonstrate that the proposed algorithm achieves higher AUC(area under curve) value, with an average increase of 11.28%, and performs superior to other algorithms such as RA indicator, LP indicator, and LRW indicator. This paper also explores the underlying mechanism that affects the model.s occurrence, from the level of intelligence generation and structure of complex systems. |
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| Keywords: | knowledge network link prediction neural network representation learning |
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