# Graph Attention

334 papers with code • 0 benchmarks • 1 datasets

## Benchmarks

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## Libraries

Use these libraries to find Graph Attention models and implementations## Most implemented papers

# Graph Attention Networks

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.

# How Attentive are Graph Attention Networks?

Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.

# GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.

# Representation Learning on Graphs with Jumping Knowledge Networks

Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

# Graph Neural Networks: A Review of Methods and Applications

Lots of learning tasks require dealing with graph data which contains rich relation information among elements.

# Inductive Representation Learning on Temporal Graphs

Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

# Graph Neural Network for Traffic Forecasting: A Survey

In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems.

# Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.

# Graph Matching Networks for Learning the Similarity of Graph Structured Objects

This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions.

# GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud

In this paper, we propose a novel neural network for point cloud, dubbed GAPNet, to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers.