Results 61 to 70 of about 1,049,471 (309)
Graph Convolutional Networks and Attention-Based Outlier Detection
IEEE Access, 2022 Outlier detection is a significant research direction in machine learning and has many applications in finance, network security, and other areas. Outlier detection of Euclidean datasets is a mainstream problem in outlier detection.
Rui Qiu +4 more
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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
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Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion
IEEE Access, 2020 Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion.
Kexi Ji, Bei Hui, Guangchun Luo
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MEGAN: Multi-explanation Graph Attention Network
, 2023 We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
Jonas Teufel +3 more
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AAAI Conference on Artificial Intelligence, 2021 In finance, the momentum spillovers of listed firms is well acknowledged. Only few studies predicted the trend of one firm in terms of its relevant firms.
Rui Cheng, Qing Li
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Gated Graph Attention Network for Cancer Prediction [PDF]
Sensors, 2021 With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the ...
Linling Qiu +3 more
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Applied Sciences, 2021 In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and ...
Chengming Liu +5 more
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Recipe Recommendation With Hierarchical Graph Attention Network [PDF]
Frontiers in Big Data, 2022 Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we
Yijun Tian +3 more
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MGATs: Motif-Based Graph Attention Networks
MathematicsIn recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks.
Jinfang Sheng +3 more
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Mahalanobis Distance-Based Graph Attention Networks
IEEE AccessIn this paper, a Mahalanobis Distance-based Graph Attention Network for graph classification, is proposed. In contrast to traditional Graph Attention Networks, the proposed approach learns the covariances between node features so as to determine the ...
Konstantina Mardani +2 more
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