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Open Access

SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs

Luxembourg Institute of Science and Technology and University of Luxembourg, Esch-Sur-Alzette L-4362, Luxembourg
Department of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UK
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Faculty of Computing, Engineering and Media, De Montfort University, Leicester, LE1 9BH, UK
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Abstract

Attributed graphs have an additional sign vector for each node. Typically, edge signs represent like or dislike relationship between the node pairs. This has applications in domains, such as recommender systems, personalised search, etc. However, limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap. Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes. The study of attributed networks is challenging due to noise, sparsity, and class imbalance issues. In this work, we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks. We propose Semi-supervised Node Classification in Attributed graphs (SNCA). SNCA is robust to underlying network noise, and has in-built class imbalance handling capabilities. We perform an extensive experimental study on real-world datasets to showcase the efficiency, scalability, robustness, and pertinence of the solution. The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.

Keywords

attributed networksnode classificationrecommender systems

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 794-808
DOI: 10.26599/BDMA.2024.9020033
Cite this article:
Abbasi F, Muzammal M, Qu Q, et al. SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs. Big Data Mining and Analytics, 2024, 7(3): 794-808. https://doi.org/10.26599/BDMA.2024.9020033

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Received: 22 December 2023
Revised: 24 April 2024
Accepted: 16 May 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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