Graph Attention

165 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Graph Attention Networks

PetarV-/GAT ICLR 2018

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.

GraphSAINT: Graph Sampling Based Inductive Learning Method

GraphSAINT/GraphSAINT ICLR 2020

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

Graph Neural Networks: A Review of Methods and Applications

thunlp/GNNPapers 20 Dec 2018

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

How Attentive are Graph Attention Networks?

tech-srl/how_attentive_are_gats ICLR 2022

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.

Inductive Representation Learning on Temporal Graphs

StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs ICLR 2020

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

jwwthu/GNN4Traffic 27 Jan 2021

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

njchoma/DGAPN ICLR 2022

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.

Representation Learning on Graphs with Jumping Knowledge Networks

dmlc/dgl ICML 2018

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

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Lin-Yijie/Graph-Matching-Networks ICLR 2019

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

FrankCAN/GAPNet 21 May 2019

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.