Nonlinear Feature Selection on Attributed Networks

The acceleratinnsional nodal attributes in various data mining tasks highlights the significance of feature selection on the networked data. Due to the lack of class labels of nodes, many feature selection methods are proposed in semi-supervised or unsupervised manners in various scenarios instead of supervised ones. More often than not, features and (pseudo) labels are correlated in a nonlinear way that is more intricate than linearity. In these circumstances, the vast majority of existing linear algorithms could not work well since they select features according to how well the feature can linearly explain the variance of labels. Moreover, although some methods focus on nonlinear feature selection, with the neglect of the link relations between data, they are difficult to be applied to attributed networks directly. In this paper, we investigate how to achieve nonlinear feature selection on attributed networks with the help of both labeled and unlabeled data. Methodologically, we first propose a novel semi-supervised nonlinear framework FS-GCN based on graph convolutional networks (GCNs) to select high-quality features, which can elaborately catch the nonlinear dependency between nodal attributes and class labels. To verify the importance of nonlinearity precisely, we further explore the possibility of totally removing the label information so that a variant of FS-GCN is proposed in the unsupervised form, referred to as UFS-GCN. Besides, experimental results on several real-world datasets validate the superiority of FS-GCN as well as UFS-GCN in terms of the quality of selected features, suggesting their robustness in the condition of extremely low even zero label ratio.