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IEEE Transactions on Neural Networks and Learning Systems, 2022
Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However,
Yuzhong Chen +10 more
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Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However,
Yuzhong Chen +10 more
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Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation
International Conference on Information and Knowledge Management, 2023Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users.
Shutong Qiao +4 more
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RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2021Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs.
Renbo Zhu, Meng Ma, Ping Wang
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TGOpt: Redundancy-Aware Optimizations for Temporal Graph Attention Networks
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2023Temporal Graph Neural Networks are gaining popularity in modeling interactions on dynamic graphs. Among them, Temporal Graph Attention Networks (TGAT) have gained adoption in predictive tasks, such as link prediction, in a range of application domains ...
Yufeng Wang, Charith Mendis
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Heterogeneous Graph Gated Attention Network
2021 International Joint Conference on Neural Networks (IJCNN), 2021Heterogeneous graph containing different types of nodes or links is one of graph types, which is most relevant to actual problems. However, the research for heterogeneous graph has not been studied adequately. In this paper, we propose a new model named Heterogeneous Graph Gated Attention Network (HGGAN) to process heterogeneous graph, including node ...
Shuai Ma +3 more
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Multi-Graph Attention Networks With Bilinear Convolution for Diagnosis of Schizophrenia
IEEE journal of biomedical and health informatics, 2023The explorations of brain functional connectivity (FC) network using resting-state functional magnetic resonance imaging (rs-fMRI) can provide crucial insights into discriminative analysis of neuropsychiatric disorders such as schizophrenia (SZ).
Renping Yu +4 more
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Dynamic Job-Shop Scheduling via Graph Attention Networks and Deep Reinforcement Learning
IEEE Transactions on Industrial InformaticsThe dynamic job-shop scheduling problem (DJSSP) is an advanced form of the classical job-shop scheduling problem (JSSP), incorporating dynamic events that make it even more challenging.
Chien-Liang Liu +2 more
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Heterogeneous Dynamic Graph Attention Network
2020 IEEE International Conference on Knowledge Graph (ICKG), 2020Network embedding (graph embedding) has become the focus of studying graph structure in recent years. In addition to the research on homogeneous networks and heterogeneous networks, there are also some methods to attempt to solve the problem of dynamic network embedding.
Qiuyan Li +3 more
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Spiking Heterogeneous Graph Attention Networks
AAAI Conference on Artificial IntelligenceReal-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the
Buqing Cao +5 more
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Person Re-identification using Heterogeneous Local Graph Attention Networks
Computer Vision and Pattern Recognition, 2021Recently, some methods have focused on learning local relation among parts of pedestrian images for person re-identification (Re-ID), as it offers powerful representation capabilities.
Zhong Zhang, Haijia Zhang, Shuang Liu
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