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Found 4 papers, 4 papers with code
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
Graphs help to define the relationships between entities in the data.
Ranked #1 on
Node Classification
on twitch-gamers
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification
With extensive experiments, we show that our proposed model outperforms the state-of-the-art GNN models with remarkable improvements up to 27. 8%.
Simplifying approach to Node Classification in Graph Neural Networks
In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance.
Improving Graph Neural Networks with Simple Architecture Design
Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model outperforms other state of the art GNN models and achieves up to 64% improvements in accuracy on node classification tasks.
Ranked #2 on
Node Classification
on Cornell
