Active Learning for Node Classification: The Additional Learning Ability from Unlabelled Nodes

• URL: http://arxiv.org/abs/2012.07065v1
• Date: Sun, 13 Dec 2020 13:59:48 GMT
• Title: Active Learning for Node Classification: The Additional Learning Ability from Unlabelled Nodes
• Authors: Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao
• Abstract summary: 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. We propose a novel latent space clustering-based active learning method for node classification (LSCALE)
• Score: 33.97571297149204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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