ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have become state-of-the-art methods in many supervised and semi-supervised graph representation learning scenarios. In order to achieve satisfactory performance, GCNs require a sufficient amount of labeled data. However, in real-world scenarios, labeled data is often expensive to obtain. Therefore, we propose ALG, a novel Active Learning framework for GCNs, which employs domain-specific intelligence to achieve much higher performance and efficiency compared to the generic AL frameworks. First, by decoupling GCN models, ALG serves as an effective and efficient AL framework for measuring and combining node representativeness and informativeness. Second, by exploiting the characteristic of the reception field in GCNs, ALG considers both the importance and correlation of nodes by proposing a new node selection metric that maximizes the effective reception field (ERF). We prove that this ERF maximization problem is NP-hard and provide an efficient algorithm accompanied with a provable approximation guarantee. The empirical studies on four public datasets demonstrate that ALG can significantly improve both the performance and efficiency of active learning for GCNs.