GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Tianxiang Zhao, Xiang Zhang, Suhang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, existing GNNs address the problem where node samples for different classes are balanced; while for many real-world scenarios, some classes may have much fewer instances than others. Directly training a GNN classifier in this case would under-represent samples from those minority classes and result in sub-optimal performance. Therefore, it is very important to develop GNNs for imbalanced node classification. However, the work on this is rather limited. Hence, we seek to extend previous imbalanced learning techniques for i.i.d data to the imbalanced node classification task to facilitate GNN classifiers. In particular, we choose to adopt synthetic minority over-sampling algorithms, as they are found to be the most effective and stable. This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs. Moreover, node attributes are high-dimensional. Directly over-sampling in the original input domain could generates out-of-domain samples, which may impair the accuracy of the classifier. We propose a novel framework, GraphSMOTE, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesize in this space to assure genuineness. In addition, an edge generator is trained simultaneously to model the relation information, and provide it for those new samples. This framework is general and can be easily extended into different variations. The proposed framework is evaluated using three different datasets, and it outperforms all baselines with a large margin.

Original languageEnglish (US)
Title of host publicationWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages833-841
Number of pages9
ISBN (Electronic)9781450382977
DOIs
StatePublished - Aug 3 2021
Event14th ACM International Conference on Web Search and Data Mining, WSDM 2021 - Virtual, Online, Israel
Duration: Mar 8 2021Mar 12 2021

Publication series

NameWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Conference

Conference14th ACM International Conference on Web Search and Data Mining, WSDM 2021
Country/TerritoryIsrael
CityVirtual, Online
Period3/8/213/12/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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