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
Recommended Citation
Wang, Kefan; An, Jing; Zhou, Mengchu; Shi, Zhe; Shi, Xudong; and Kang, Qi, "Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People" (2023). Faculty Publications. 2337.
https://digitalcommons.njit.edu/fac_pubs/2337