The composition of neighborhoods is crucial for the spatial domain-based Graph Convolutional Network (GCN) model. To solve the problem that the structural influence is not considered in the neighborhood ordering of nodes in the model, a novel neighborhood selection strategy was proposed to obtain an improved GCN model. Firstly, the structurally important neighborhoods were collected for each node and the core neighborhoods were selected hierarchically. Secondly, the features of the nodes and their core neighborhoods were organized into a matrix. Finally, the matrix was sent to deep Convolutional Neural Network (CNN) for semi-supervised learning. The experimental results on Cora, Citeseer and Pubmed citation network datasets show that, the proposed model has a better accuracy in node classification tasks than the model based on classical graph embedding and four state-of-the-art GCN models. As a spatial domain-based GCN, the proposed model can be effectively applied to the learning tasks of large-scale networks.

Because the existing Web quality assessment approaches rely on trained models, and users' interactions not only cannot meet the requirements of online response, but also can not capture the semantics of Web content, a data Quality Assessment based on Simulated Annealing (QASA) method was proposed. Firstly, the relevant space of the target article was constructed by collecting topic-relevant articles on the Web. Then, the scheme of open information extraction was employed to extract Web articles' facts. Secondly, Simulated Annealing (SA) was employed to construct the dimension baselines of two most important quality dimensions, namely accuracy and completeness. Finally, the data quality dimensions were quantified by comparing the facts of target article with those of the dimension baselines. The experimental results show that QASA can find the near-optimal solutions within the time window while achieving comparable or even 10 percent higher accuracy with regard to the related works. The QASA method can precisely grasp data quality in real-time, which caters for the online identification of high-quality Web articles.