Graph Attention
239 papers with code • 0 benchmarks • 1 datasets
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Libraries
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NBA player performance prediction dataset
Most implemented papers
Graph Attention Networks
PetarV-/GAT
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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
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ICLR 2020
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
How Attentive are Graph Attention Networks?
tech-srl/how_attentive_are_gats
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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.
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
StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
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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
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.
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 Matching Networks for Learning the Similarity of Graph Structured Objects
Lin-Yijie/Graph-Matching-Networks
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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
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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.
