Results 31 to 40 of about 6,341,676 (193)
SGAT: Simplicial Graph Attention Network
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes.
See Hian Lee, Feng Ji, Wee Peng Tay
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Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification
, 2023 Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes.
Shao, M +4 more
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LEHAN: Link-Feature Enhanced Heterogeneous Graph Attention Network
IEEE Access, 2022 Graph Neural Networks (GNNs) have been studied extensively and have performed well in solving complex machine learning tasks in recent years. Many GNN-based approaches focused on representing homogeneous graphs with only a single type of nodes and links.
Jongmin Park, Sungsu Lim
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Directional Graph Attention Networks
, 2023 In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated ...
Sitao Luan, Jiaqi Zhu
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How Attentive are Graph Attention Networks?
CoRR, 2021 Published in ICLR ...
Shaked Brody, Uri Alon 0002, Eran Yahav
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GAEN: Graph Attention Evolving Networks [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single
Min Shi 0001 +5 more
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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting
Mathematics, 2022 Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
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CoRR, 2017 We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Veličković, P +5 more
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Dynamic Graph Attention-Aware Networks for Session-Based Recommendation [PDF]
, 2020 Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items.
Abugabah, Ahed +2 more
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Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
Frontiers in Genetics, 2022 With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity.
Xiang Feng +4 more
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