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Journal article
NODE-SELECT: A graph neural network based on a selective propagation technique
NEUROCOMPUTING, v 494, p396
14 Jul 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure. Additionally, a very important challenge that significantly affects graph neural networks is the issue of scalability which limits their application to larger graphs. In this paper we propose our method named NODE-SELECT: an efficient graph neural network that uses subsetting layers which only allow the best sharing-fitting nodes to propagate their information. By having a selection mechanism within each layer which we stack in parallel, our proposed method NODE-SELECT is able to both reduce the amount noise propagated and adapt the restrictive sharing concept observed in real world graphs. Our NODE-SELECT significantly outperformed existing GNN frameworks in noise experiments and matched state-of-the art results in experiments without noise over different benchmark datasets. (c) 2022 Elsevier B.V. All rights reserved.
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Details
- Title
- NODE-SELECT: A graph neural network based on a selective propagation technique
- Publication Details
- NEUROCOMPUTING, v 494, p396
- Publisher
- ELSEVIER; AMSTERDAM
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000800191800015
- Scopus ID
- 2-s2.0-85129557062
- Other Identifier
- 991021861191504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence