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Graph attention neural network for water network partitioning
Applied Water Science, 2022 Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge.
Kezhen Rong +7 more
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A Regularized Attention Mechanism for Graph Attention Networks [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes ...
Uday Shankar Shanthamallu +2 more
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Knowledge-Enhanced Graph Attention Network for Fact Verification
Mathematics, 2021 Fact verification aims to evaluate the authenticity of a given claim based on the evidence sentences retrieved from Wikipedia articles. Existing works mainly leverage the natural language inference methods to model the semantic interaction of claim and ...
Chonghao Chen +2 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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Heterophily-aware graph attention network
Pattern Recognition, 2023 Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess ...
Junfu Wang +3 more
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Graph hierarchical dwell-time attention network for session-based recommendation [PDF]
ITM Web of Conferences, 2022 Session-based recommendation (SBR) is making item recommendations based on anonymous click behavior. SBR based on graph neural networks has shown great power in recent years. It can enhance the representation of items in a session.
Rong Honghui, Zhu Wenjun, Zhu Cui
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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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Enabling inductive knowledge graph completion via structure-aware attention network
, 2023 : Knowledge graph completion (KGC) aims at complementing missing entities and relations in a knowledge graph (KG). Popular KGC approaches based on KG embedding are typically limited to the transductive setting, i.e., all entities must be seen during ...
Jin, Qun +9 more
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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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