Much of the recent progress made in node classification on graphs can be credited to the careful design on graph neural networks (GNN) and label propagation algorithms . However, in the literature, in addition to improvements to the model architecture , there are a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use . In this paper, we first summarize a collection of existing refinements , and then propose several novel techniques regarding these model designs and label usage . We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs . However, in addition to their reliance on elaborate architectures and algorithms , there are several key technical details that are frequently overlooked, and yet nonetheless can play a vital role in achieving satisfactory performance . In this paper, we first summarize a series of existing tricks-of-the-trade , and then propose several new ones related to label usage, loss function formulation, and model design that can significantly improve various GNN architectures . We empirically evaluate their impact on final node classification accuracy by conducting ablation studies and demonstrate consistently-improved performance, often to an extent that outweighs the gains from more dramatic changes in the underlying GNN architecture . Notably, many of the top-ranked models on the Open Graph Benchmark (OGB) leaderboard benefit from our techniques.
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Much of the recent progress made in node classification on graphs can be credited to the careful design on
Over the past few years,
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propagation algorithms
propagation-based methods have made significant progress in addressing node classification tasks on graphs
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the literature, in addition to improvements to the model architecture
addition to their reliance on elaborate architectures and algorithms
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a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use
several key technical details that are frequently overlooked, and yet nonetheless can play a vital role in achieving satisfactory performance
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collection of existing refinements
series of existing tricks-of-the-trade
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novel techniques regarding these model designs and label usage
new ones related to label usage, loss function formulation, and model design that can significantly improve various GNN architectures
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impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh
impact on final node classification accuracy by conducting ablation studies and demonstrate consistently-improved performance, often to an extent that outweighs
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model architectureimprovement
more dramatic changes in the underlying GNN architecture
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the
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(OGB) leaderboard