Classification of data points which correspond to complex entities such as people or journal articles is a ongoing research task. Notable applications are recommendation systems for customer behaviors based upon their features or past purchases and in academia labeling relevant research papers in order to reduce the reading time required. The features that can be extracted are many and result in large datasets which are a challenge to process with complex machine learning methodologies. There is also an issue on how this is presented and how to interpret the parameterizations beyond the classification accuracies. This work shows how the network information contained in an adjacency matrix allows improved classification of entities through their associations and how the framework of the SGC provide an expressive and fast approach. The proposed regularized SGC incorporates shrinkage upon three different aspects of the projection vectors to reduce the number of parameters, the size of the parameters and the directions between the vectors to produce more meaningful interpretations.

与诸如人或期刊文章之类的复杂实体相对应的数据点的分类是一项正在进行的研究任务。值得注意的应用是基于客户特征或过往购买行为的客户行为推荐系统，并在学术界标记相关研究论文，以减少所需的阅读时间。可以提取的特征很多，并且会导致数据集庞大，这对于使用复杂的机器学习方法进行处理是一个挑战。关于如何呈现以及如何解释超出分类精度的参数化，也存在一个问题。这项工作显示了邻接矩阵中包含的网络信息如何通过实体之间的关联来改进实体的分类，以及SGC的框架如何提供一种表达力强且快速的方法。