{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dimensionality-reduction-meets-message","title":"Dimensionality Reduction Meets Message Passing for Graph Node Embeddings","arxiv_id":"2202.00408","date":"2022-02-01","proceeding":null,"authors":["Krzysztof Sadowski","Michał Szarmach","Eddie Mattia"],"abstract":"Graph Neural Networks (GNNs) have become a popular approach for various applications, ranging from social network analysis to modeling chemical properties of molecules. While GNNs often show remarkable performance on public datasets, they can struggle to learn long-range dependencies in the data due to over-smoothing and over-squashing tendencies. To alleviate this challenge, we propose PCAPass, a method which combines Principal Component Analysis (PCA) and message passing for generating node embeddings in an unsupervised manner and leverages gradient boosted decision trees for classification tasks. We show empirically that this approach provides competitive performance compared to popular GNNs on node classification benchmarks, while gathering information from longer distance neighborhoods. Our research demonstrates that applying dimensionality reduction with message passing and skip connections is a promising mechanism for aggregating long-range dependencies in graph structured data.","url_abs":"https://arxiv.org/abs/2202.00408v2","url_pdf":"https://arxiv.org/pdf/2202.00408v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dimensionality-reduction-meets-message","repo_url":"https://github.com/ksadowski13/PCAPass","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"node-property-prediction","task_name":"Node Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-reddit","task":"Node Classification","dataset":"Reddit","model":"PCAPass + XGBoost","rank_in_archive_order":10,"of":16,"metrics":{"Accuracy":"96.26 ± 0.02%"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-arxiv","task":"Node Property Prediction","dataset":"ogbn-arxiv","model":"PCAPass + XGBoost","rank_in_archive_order":75,"of":86,"metrics":{"Ext. data":"No","Number of params":"0","Test Accuracy":"0.7187 ± 0.0003","Validation Accuracy":"0.7325 ± 0.0005"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-papers100m","task":"Node Property Prediction","dataset":"ogbn-papers100M","model":"PCAPass + LightGBM","rank_in_archive_order":16,"of":20,"metrics":{"Ext. data":"No","Number of params":"0","Test Accuracy":"0.6591 ± 0.0003","Validation Accuracy":"0.6982 ± 0.0002"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-products","task":"Node Property Prediction","dataset":"ogbn-products","model":"PCAPass + XGBoost","rank_in_archive_order":39,"of":64,"metrics":{"Ext. data":"No","Number of params":"0","Test Accuracy":"0.8115 ± 0.0002","Validation Accuracy":"0.9200 ± 0.0005"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}