Context|Aware Graph Attention Networks

[1910.01736] Context-Aware Graph Attention Networks - arXiv

CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges.

Sequential Recommendation with Context-Aware Collaborative ...

In this paper, we propose a method named Contextual Collaborative Graph Attention Network (CCGAT) to model the sequence.

Context-aware Heterogeneous Graph Attention Network for User ...

In this paper, we propose a context-aware heterogeneous graph attention network (CHGAT) to dynamically generate the representation of the user and to estimate ...

[PDF] Context-Aware Graph Attention Networks - Semantic Scholar

A novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT), which aims to learn a context-aware attention representation for ...

Graph-Context Attention Networks for Size-Varied Deep Graph ...

[14] further developed a matching aware embed- ding model, where the predicted soft assignment score is concatenated to node embeddings. To address the combi-.

Graph Attention Networks. Graph Machine Learning | by Ashish Kumar

Graph attention networks (GATs) can learn from graph-structured data, such as social networks, citation networks, or knowledge graphs.

Graph Attention Network for Context-Aware Visual Tracking - PubMed

We propose a simple context-aware Siamese graph attention network, which establishes part-to-part correspondence between the Siamese branches with a complete ...

HCAG: A Hierarchical Context-Aware Graph Attention Model for ...

Our model mirrors the hierarchical structure of depression assessment and leverages the Graph Attention Network (GAT) to grasp relational contextual information ...

Dual syntax aware graph attention networks with prompt for aspect ...

Recently, pre-trained language models like BERT have been widely used as context encoders in ABSA. Graph neural networks have also been ...

Context-Aware Graph Attention Networks | Request PDF

Request PDF | Context-Aware Graph Attention Networks | Graph Neural Networks (GNNs) have been widely studied for graph data representation ...

Contrastive multi-interest graph attention network for knowledge ...

Specifically, CMGAN employs a collaborative knowledge graph encoder, enhancing node representations through relational-aware embedding aggregation. Then a ...

Graph Attention Networks: A Comprehensive Review of Methods ...

Spatial graph attention networks (spatial GATs) are specialized graph neural networks designed to focus specifically on the spatial relationships between nodes ...

Preference-aware Graph Attention Networks for Cross-Domain ...

In this article, we propose a Preference-aware Graph Attention network model with Collaborative Knowledge Graph (PGACKG) for cross-domain recommendations.

Relation-aware Graph Attention Networks with Relational Position ...

Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account. In particular, the state- ...

sunxiaobei/awesome-attention-based-gnns - GitHub

[AAAI2020] [TALP] Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network [paper] ... Context Enhanced Graph Neural ...

Graph4Web: A relation-aware graph attention network for web ...

In this work, we propose Graph4Web, which uses a relation-aware graph attention network for web service classification.

LaGAT: link-aware graph attention network for drug–drug interaction ...

We propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based ...

FairGAT: Fairness-Aware Graph Attention Networks

... context of group fairness [41]. For example, the accuracy differences between groups of people from different ethnicities in a face ...

Graph Attention | Papers With Code

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers.

[PDF] Graph Attention Networks - Semantic Scholar

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers.