Node Property Prediction
50 papers with code • 5 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Node Property Prediction
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Libraries
Use these libraries to find Node Property Prediction models and implementations
OGB
Most implemented papers
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.
Heterogeneous Graph Transformer
acbull/pyHGT
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Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
SIGN: Scalable Inception Graph Neural Networks
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.
Simple and Deep Graph Convolutional Networks
We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks.
Training Graph Neural Networks with 1000 Layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
amzn/pecos
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ICLR 2022
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
SCR: Training Graph Neural Networks with Consistency Regularization
thudm/scr
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However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
DeeperGCN: All You Need to Train Deeper GCNs
dmlc/dgl
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Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Towards Deeper Graph Neural Networks
Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
