Results 31 to 40 of about 1,049,471 (309)
MBHAN: Motif-Based Heterogeneous Graph Attention Network
Applied Sciences, 2022 Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu +3 more
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Attention-aware heterogeneous graph neural network [PDF]
Big Data Mining and Analytics, 2021 As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks. It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network (HIN). The main reason for
Jintao Zhang, Quan Xu
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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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Multi-hop Attention Graph Neural Networks [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
Wang, Guangtao +3 more
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DualGATs: Dual Graph Attention Networks for Emotion Recognition in Conversations
Annual Meeting of the Association for Computational Linguistics, 2023 Capturing complex contextual dependencies plays a vital role in Emotion Recognition in Conversations (ERC). Previous studies have predominantly focused on speaker-aware context modeling, overlooking the discourse structure of the conversation.
Duzhen Zhang, Feilong Chen, Xiuyi Chen
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Graph Attention Networks for Anti-Spoofing [PDF]
Interspeech, 2021 The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments.
Hemlata Tak +4 more
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Knowledge Graph Embedding via Graph Attenuated Attention Networks
IEEE Access, 2020 Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang +4 more
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Enhancing Facial Reconstruction Using Graph Attention Networks
IEEE Access, 2023 Traditionally, research on three-dimensional (3D) facial reconstruction has focused heavily on methods that use 3D Morphable Models (3DMMs) based on principal component analysis (PCA).
Hyeong Geun Lee +3 more
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Graph Ordering Attention Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function.
Chatzianastasis, Michail +3 more
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Ensemble Graph Attention Networks
Transactions on Machine Learning and Artificial Intelligence, 2022 Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked.
Nan Wu, Chaofan Wang
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