  Node classification on graph data is an important task on many practical
domains. However, it requires labels for training, which can be difficult or
expensive to obtain in practice. Given a limited labelling budget, active
learning aims to improve performance by carefully choosing which nodes to
label. Our empirical study shows that existing active learning methods for node
classification are considerably outperformed by a simple method which randomly
selects nodes to label and trains a linear classifier with labelled nodes and
unsupervised learning features. This indicates that existing methods do not
fully utilize the information present in unlabelled nodes as they only use
unlabelled nodes for label acquisition. In this paper, we utilize the
information in unlabelled nodes by using unsupervised learning features. We
propose a novel latent space clustering-based active learning method for node
classification (LSCALE). Specifically, to select nodes for labelling, our
method uses the K-Medoids clustering algorithm on a feature space based on the
dynamic combination of both unsupervised features and supervised features. In
addition, we design an incremental clustering module to avoid redundancy
between nodes selected at different steps. We conduct extensive experiments on
three public citation datasets and two co-authorship datasets, where our
proposed method LSCALE consistently and significantly outperforms the
state-of-the-art approaches by a large margin.
