Results 41 to 50 of about 8,080,419 (296)
Temporal Graph Attention Network for Building Thermal Load Prediction
, 2023 Temporal Graph Attention Network for Building Thermal Load ...
M Reza Hosseini (13630525) +5 more
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hpGAT: High-Order Proximity Informed Graph Attention Network
IEEE Access, 2019 Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data. However, most existing GNNs limit the receptive field of the node on each layer to its connected (one-hop) neighbors, which ...
Zhining Liu +4 more
doaj +1 more source
A Graph Positional Attention Network for Session-Based Recommendation
IEEE Access, 2023 The main idea of a session-based recommendation system is to model the user’s historical click sequence and then summarize user preferences and predict the items the user will interact with.
Liyan Dong +5 more
doaj +1 more source
SEA: Graph Shell Attention in Graph Neural Networks
, 2023 A common issue in Graph Neural Networks (GNNs) is known as over-smoothing. By increasing the number of iterations within the message-passing of GNNs, the nodes' representations of the input graph align with each other and become indiscernible. Recently, it has been shown that increasing a model's complexity by integrating an attention mechanism yields ...
Christian M. M. Frey +2 more
openaire +2 more sources
Adaptive Depth Graph Attention Networks
CoRR, 2023 As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results. However, since GAT was proposed, none of the existing studies have provided systematic insight into the ...
Jingbo Zhou +3 more
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Graph Attention Networks with Positional Embeddings [PDF]
, 2021 Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighboring nodes having similar features and labels--, and therefore may not be at their full potential when dealing with non-homophilic graphs.
Liheng Ma +2 more
openaire +3 more sources
Dual-Attention Graph Convolutional Network [PDF]
, 2020 Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity.
Xueya Zhang +4 more
openaire +3 more sources
Solar Wind Speed Prediction via Graph Attention Network
Space Weather, 2022 The solar wind is a plasma flow formed by the expansion of the high‐temperature corona and propagates in the interplanetary space with speeds between 200 km/s and 900 km/s.
Yanru Sun +4 more
doaj +1 more source
Automatic Requirements Classification Based on Graph Attention Network
IEEE Access, 2022 Requirements classification is a significant task for requirements engineering, which is time-consuming and challenging. The traditional requirements classification models usually rely on manual pre-processing and have poor generalization capability ...
Gang Li +3 more
doaj +1 more source
Predicting Protein–Protein Interactions via Gated Graph Attention Signed Network
, 2021 Protein–protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning.
Zhijie Xiang +5 more
core +1 more source