Ma Yugang, Li Qing, Hu Nan, Li Lili
School of Architecture and Urban Planning, Chongqing University, Chongqing, China.
School of Computer Science, Northwestern Polytechnical University, Shaanxi, China.
Front Neurorobot. 2021 Apr 1;15:665055. doi: 10.3389/fnbot.2021.665055. eCollection 2021.
Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated.
用于生物医学图谱和先进制造图谱的半监督深度学习正迅速成为学术界和工业界的一个重要话题。许多现有类型的研究集中在半监督链接预测和节点分类，以及这些方法在可持续发展和先进制造中的应用。迄今为止，大多数制造图神经网络主要在社交和信息网络上进行评估，通过整合邻居节点描述来提高网络表示的质量。然而，以前的方法尚未在生物医学网络上进行全面研究。传统技术无法取得令人满意的结果，尤其是在标记节点数量不足时。本文提出了一种通过可持续知识转移用于生物医学图谱的新的半监督深度学习方法，称为SeBioGraph。在SeBioGraph中，节点嵌入和特定于图的原型嵌入都被用作可转移的度量空间特征。通过纳入从辅助图中学到的先验知识，SeBioGraph进一步提升了目标图的性能。在二分类节点分类任务和三分类链接预测任务上的实验结果表明，SeBioGraph实现了当前最优的结果。最后，对该方法进行了全面评估。
