Results 11 to 20 of about 106,924 (261)
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
openaire +5 more sources
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
core +1 more source
Multiscale Global Adaptive Attention Graph Neural Network [PDF]
, 2023 Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields.
GOU Ruru, YANG Wenzhu, LUO Zifei, YUAN Yunfeng
core +1 more source
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
core +1 more source
How Attentive are Graph Attention Networks?
CoRR, 2021 Published in ICLR ...
Shaked Brody, Uri Alon 0002, Eran Yahav
openaire +3 more sources
Attention-Aware Heterogeneous Graph Neural Network [PDF]
, 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.
Jintao Zhang, Quan Xu
core +1 more source
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
openaire +3 more sources
Graph Attention Networks for Anti-Spoofing [PDF]
Interspeech 2021, 2021 Submitted to INTERSPEECH ...
Hemlata Tak +4 more
openaire +2 more sources
, 2022 Dynamic graph neural networks (DGNNs) have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss, or ...
Liu, Chao +15 more
core +1 more source
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
openaire +2 more sources