Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.

由多种类型的节点和链接组成的异构信息网络（HIN）在现实生活中越来越重要。相关数据的分类是网络分析中必不可少的工作。现有方法将这些分类任务应用于同质信息网络和简单数据，但不适用于嘈杂和稀疏数据时，可以有效地解决这些分类任务。为了解决该问题，我们提出了具有稀疏因子的堆叠式降噪自动编码器（SDAE），以学习异构网络中节点的特征。特别地，稀疏因子被添加到所提议的堆叠式去噪自动编码器的每个隐藏层中，以有效地从噪声和稀疏数据中提取特征。此外，采用放松策略以高质量为基础构建类层次结构。最后，可以对异构信息网络的节点进行分类。我们提出的具有稀疏因子的堆叠式去噪自动编码器（RSDAEf）的框架松弛策略与几种现有方法的比较清楚地表明，RSDAEf优于现有方法，并且在DBLP数据集上实现了88.3％的分类精度。