"Minority-Weighted Graph Neural Network for Imbalanced Node Classificat" by Kefan Wang, Jing An et al.
 

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

Document Type

Article

Publication Date

1-1-2023

Abstract

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.

Identifier

85137565495 (Scopus)

Publication Title

IEEE Internet of Things Journal

External Full Text Location

https://doi.org/10.1109/JIOT.2022.3200964

e-ISSN

23274662

First Page

330

Last Page

340

Issue

1

Volume

10

Grant

2021- cyxt2-kj10

Fund Ref

National Natural Science Foundation of China

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