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Adaptive graph convolutional collaboration networks for semi-supervised classification

INFORMATION SCIENCES (2022)

Exploiting the potentialities of features for speech emotion recognition

Dongdong Li et al.

Summary: Recent studies on speech signals have focused on emotional information and the importance of feature representation in speech emotion recognition (SER). Different combinations of features and models have a significant impact on SER performance, with the proposed ECFW method showing promising results in improving performance across different databases.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Computer Science, Information Systems

Semi-supervised classification by graph p-Laplacian convolutional networks

Sichao Fu et al.

Summary: This paper introduces the graph p-Laplacian convolutional networks (GpLCN) to better extract sample features and improve classification performance by utilizing graph p-Laplacian matrix.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems