Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
This addresses the challenge of expensive label acquisition in graph-based machine learning, offering a practical solution for domains like network analysis, though it appears incremental as it builds on existing graph neural network and active learning techniques.
The paper tackles the problem of node classification in attributed graphs with limited labeled data by proposing a graph-based active learning algorithm that uses graph cognizant logistic regression and preemptive query generation, achieving significant improvements over state-of-the-art methods on five public benchmark datasets and a private dataset.
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can improve the achieved classification performance for a given budget on the number of queried labels. The best existing methods are based on graph neural networks, but they often perform poorly unless a sizeable validation set of labelled nodes is available in order to choose good hyperparameters. We propose a novel graph-based active learning algorithm for the task of node classification in attributed graphs; our algorithm uses graph cognizant logistic regression, equivalent to a linearized graph convolutional neural network (GCN), for the prediction phase and maximizes the expected error reduction in the query phase. To reduce the delay experienced by a labeller interacting with the system, we derive a preemptive querying system that calculates a new query during the labelling process, and to address the setting where learning starts with almost no labelled data, we also develop a hybrid algorithm that performs adaptive model averaging of label propagation and linearized GCN inference. We conduct experiments on five public benchmark datasets, demonstrating a significant improvement over state-of-the-art approaches and illustrate the practical value of the method by applying it to a private microwave link network dataset.