Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

TL;DR

This paper introduces a novel active learning algorithm for node classification on attributed graphs that leverages graph cognizant logistic regression and preemptive query generation, outperforming existing methods especially with limited labeled data.

Contribution

The paper presents a new active learning approach using linearized GCNs, preemptive querying, and hybrid label propagation, addressing hyperparameter tuning and initial label scarcity issues.

Findings

Significant performance improvement over state-of-the-art methods.

Effective in scenarios with very few labeled nodes.

Validated on five benchmark datasets and a real-world microwave network dataset.

Abstract

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.