MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification

Zhang, L; Wang, S; Liu, J; Lin, Q; Chang, X; Wu, Y; Zheng, Q

MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification

Zhang, L Wang, S Liu, J Lin, Q Chang, X

Wu, Y Zheng, Q

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2022, PP, (99), pp. 1-1
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Zhang, L
dc.contributor.author	Wang, S
dc.contributor.author	Liu, J
dc.contributor.author	Lin, Q
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Wu, Y
dc.contributor.author	Zheng, Q
dc.date.accessioned	2023-03-31T04:10:08Z
dc.date.available	2023-03-31T04:10:08Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2022, PP, (99), pp. 1-1
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/168925
dc.description.abstract	Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much attention due to expensive cost of data annotation. Various meta-learning methods have made a great progress for few-shot problem in image and text data. In reality, data samples are not independent but rich in link relations. Large amounts of data exists in the form of graph structure such as citation, social, and biological networks. However, FSL study on graph data is still in its infancy because of the obstacle on extracting meta-knowledge from a meta node classification task. Current research just simply combines the FSL methods experienced in computer vision with node representation models together, but ignores the effect of rich links among support and query nodes in few-shot meta-task. For this issue, we propose a novel Multi-Level Graph Relation Network (MuL-GRN) for the challenging few-shot node classification. MuL-GRN extracts node embeddings through the popular graph neural networks (GNNs). And it includes a relation learning module to mine the deep node relations from three views, namely node-level, global subgraph-level, and local subgraph-level relations. For any two nodes, the node-level relation is computed on their node embeddings, global subgraph-level relation is measured on their subgraph embeddings, and the local subgraph-level relation is mined according to the pairwise node comparison information in their subgraphs. The three-view relation vectors are fused together with an interesting relation fusion module, which measures the importance of relation vector for the current few-shot classification task automatically. Extensive experiments on five real datasets show that MuL-GRN significantly outperforms existing state-of-the-art methods by a large margin.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2022.3176880
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T04:10:03Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much attention due to expensive cost of data annotation. Various meta-learning methods have made a great progress for few-shot problem in image and text data. In reality, data samples are not independent but rich in link relations. Large amounts of data exists in the form of graph structure such as citation, social, and biological networks. However, FSL study on graph data is still in its infancy because of the obstacle on extracting meta-knowledge from a meta node classification task. Current research just simply combines the FSL methods experienced in computer vision with node representation models together, but ignores the effect of rich links among support and query nodes in few-shot meta-task. For this issue, we propose a novel Multi-Level Graph Relation Network (MuL-GRN) for the challenging few-shot node classification. MuL-GRN extracts node embeddings through the popular graph neural networks (GNNs). And it includes a relation learning module to mine the deep node relations from three views, namely node-level, global subgraph-level, and local subgraph-level relations. For any two nodes, the node-level relation is computed on their node embeddings, global subgraph-level relation is measured on their subgraph embeddings, and the local subgraph-level relation is mined according to the pairwise node comparison information in their subgraphs. The three-view relation vectors are fused together with an interesting relation fusion module, which measures the importance of relation vector for the current few-shot classification task automatically. Extensive experiments on five real datasets show that MuL-GRN significantly outperforms existing state-of-the-art methods by a large margin.

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http://hdl.handle.net/10453/168925