LSCALE: Latent Space Clustering-Based Active Learning for Node Classification

Juncheng Liu*, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Node classification on graphs is an important task in many practical domains. It usually requires labels for training, which can be difficult or expensive to obtain in practice. Given a budget for labelling, active learning aims to improve performance by carefully choosing which nodes to label. Previous graph active learning methods learn representations using labelled nodes and select some unlabelled nodes for label acquisition. However, they do not fully utilize the representation power present in unlabelled nodes. We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification. In this paper, we propose a latent space clustering-based active learning framework for node classification (LSCALE), where we fully utilize the representation power in both labelled and unlabelled nodes. Specifically, to select nodes for labelling, our framework uses the K-Medoids clustering algorithm on a latent space based on a 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. Extensive experiments on five datasets show that our proposed framework LSCALE consistently and significantly outperforms the state-of-the-art approaches by a large margin.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I
EditorsMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
PublisherSpringer Cham
Pages55-70
Number of pages16
Edition1st
ISBN (Electronic)9783031263873
ISBN (Print)9783031263866
DOIs
Publication statusPublished - 17 Mar 2023
Event22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, France
Duration: 19 Sept 202223 Sept 2022
https://link.springer.com/book/10.1007/978-3-031-26387-3
https://link.springer.com/book/10.1007/978-3-031-23618-1

Publication series

NameLecture Notes in Computer Science
Volume13713
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
NameECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference

Conference22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
Country/TerritoryFrance
CityGrenoble
Period19/09/2223/09/22
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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