计算机科学
人工智能
无监督学习
机器学习
图形
代表(政治)
特征学习
自然语言处理
理论计算机科学
政治
政治学
法学
作者
Dongxiao He,Chundong Liang,Cuiying Huo,Zhiyong Feng,Dayong Jin,Lijian Yang,Weixiong Zhang
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-13
被引量:9
标识
DOI:10.1109/tnnls.2022.3149997
摘要
Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.
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