Aspect sentiment analysis with heterogeneous graph neural networks

Article Computer Science, Artificial Intelligence

Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer's Disease analysis

Yonghua Zhu et al.

Summary: This paper proposes a GCN architecture combining interpretable feature learning and dynamic graph learning for personalized early Alzheimer's disease diagnosis, which outputs competitive diagnosis performance and provides interpretability.

INFORMATION FUSION (2022)

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Article Computer Science, Artificial Intelligence

Reverse Graph Learning for Graph Neural Network

Liang Peng et al.

Summary: Graph neural networks (GNNs) conduct feature learning by considering the local structure preservation of the data, but face issues with faulty and missing edges in the initial graph and the inability to generate predictions for unseen data. This study proposes a reverse GNN model to learn a high-quality graph from the intrinsic space of the original data points and introduces a new out-of-sample extension method.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Computer Science, Interdisciplinary Applications

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification

Rongyao Hu et al.

Summary: A novel functional connectivity analysis framework is proposed to conduct joint feature learning and personalized disease diagnosis in a semi-supervised manner, focusing on putative multi-band functional connectivity biomarkers.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

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Article Computer Science, Information Systems

Adaptive reverse graph learning for robust subspace learning

Changan Yuan et al.

Summary: Subspace learning reduces the dimensions of high-dimensional data by projecting them into a low-dimensional subspace while preserving data similarity. Two strategies, reverse graph embedding and robust estimators, are designed to minimize the impact of redundancy and noise in the original space. Experiments show that this approach outperforms other state-of-the-art subspace learning methods across different evaluation metrics.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Computer Science, Hardware & Architecture

Learning Representation From Concurrence-Words Graph For Aspect Sentiment Classification

Guangquan Lu et al.

Summary: Aspect sentiment classification is an important research topic, and this paper introduces a network framework to improve the classification by learning representation from a concurrence-words relation graph. Experimental results show a significant improvement in prediction performance across five benchmark datasets.

COMPUTER JOURNAL (2021)

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Article Computer Science, Information Systems