Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

Kefan Wang, Jing An, Mengchu Zhou, Zhe Shi, Xudong Shi, Qi Kang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the assumption of balanced data distribution and ignore the imbalanced problem of social networks. In order to extract the valuable information from imbalanced data for decision making, a novel method named minority-weighted graph neural network (mGNN) is presented in this article. It extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks. In a node feature aggregation stage, the node membership values among nodes are calculated for minority nodes' feature aggregation enhancement. In an oversampling stage, the cost-sensitive learning is used to improve edge prediction results of synthetic minority nodes, and further raise their importance. In addition, a Gumbel distribution is adopted as an activation function. The proposed mGNN is evaluated on six social network data sets. Experimental results show that it yields promising results for imbalanced node classification.

Original languageEnglish (US)
Pages (from-to)330-340
Number of pages11
JournalIEEE Internet of Things Journal
Issue number1
StatePublished - Jan 1 2023

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Graph neural networks (GNNs)
  • Gumbel distribution
  • Internet of People (IoP)
  • imbalanced node classification
  • social networks


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