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Graph Neural Networks (GNNs) have become a popular approach for variousapplications, ranging from social network analysis to modeling chemicalproperties of molecules. While GNNs often show remarkable performance on publicdatasets, they can struggle to learn long-range dependencies in the data due toover-smoothing and over-squashing tendencies. To alleviate this challenge, wepropose PCAPass, a method which combines Principal Component Analysis (PCA) andmessage passing for generating node embeddings in an unsupervised manner andleverages gradient boosted decision trees for classification tasks. We showempirically that this approach provides competitive performance compared topopular GNNs on node classification benchmarks, while gathering informationfrom longer distance neighborhoods. Our research demonstrates that applyingdimensionality reduction with message passing and skip connections is apromising mechanism for aggregating long-range dependencies in graph structureddata.

Dimensionality Reduction Meets Message Passing for Graph Node Embeddings