{"url":"/sota/node-classification-on-aifb","task":{"name":"Node Classification","url":"/task/node-classification","note":null},"dataset":{"name":"AIFB","url":null},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"**Node Classification** is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.\r\n\r\n**Node Classification** models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like [Cora](/dataset/cora), [Citeseer](/dataset/citeseer), and [Pubmed](/dataset/pubmed), among others, typically using Accuracy and F1.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Fast Graph Representation Learning With PyTorch Geometric](https://arxiv.org/pdf/1903.02428v3.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":7,"rows_with_code":7,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"R-GCN","metrics":{"Accuracy":"95.83"},"uses_additional_data":false,"paper_date":"2017-03-17","paper":"/paper/modeling-relational-data-with-graph","paper_url":"http://arxiv.org/abs/1703.06103v4","paper_title":"Modeling Relational Data with Graph Convolutional Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn","n_code_links":27,"syntology":{"n_ran":10,"n_unverified":22,"n_samples":32,"n_pointer_only_licence":15}},{"rank_in_archive_order":2,"model":"RR-GCN-PPV-CUT","metrics":{"Accuracy":"95.83"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/r-gcn-the-r-could-stand-for-random","paper_url":"https://arxiv.org/abs/2203.02424v2","paper_title":"R-GCN: The R Could Stand for Random","code":"https://github.com/predict-idlab/RR-GCN","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"SCENE","metrics":{"Accuracy":"95.83"},"uses_additional_data":false,"paper_date":"2023-01-09","paper":"/paper/scene-reasoning-about-traffic-scenes-using","paper_url":"https://arxiv.org/abs/2301.03512v1","paper_title":"SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks","code":"https://github.com/schmidt-ju/scene","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"BoP","metrics":{"Accuracy":"92.22"},"uses_additional_data":false,"paper_date":"2024-11-17","paper":"/paper/from-primes-to-paths-enabling-fast-multi","paper_url":"https://arxiv.org/abs/2411.11149v1","paper_title":"From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis","code":"https://github.com/kbogas/PAM_BoP","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"Path Tree","metrics":{"Accuracy":"89.44"},"uses_additional_data":false,"paper_date":"2019-08-22","paper":"/paper/inducing-a-decision-tree-with-discriminative","paper_url":"http://ceur-ws.org/Vol-2427/SEPDA_2019_paper_3.pdf","paper_title":"Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph","code":"https://github.com/IBCNServices/KGPTree","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"RDF2Vec+SVM","metrics":{"Accuracy":"88.88"},"uses_additional_data":false,"paper_date":"2017-11-10","paper":"/paper/rdf2vec-rdf-graph-embeddings-and-their","paper_url":"http://www.semantic-web-journal.net/content/rdf2vec-rdf-graph-embeddings-and-their-applications-1","paper_title":"RDF2Vec: RDF Graph Embeddings and Their Applications","code":"https://github.com/IBCNServices/pyRDF2Vec","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"RR-GCN-PPV","metrics":{"Accuracy":"86.11"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/r-gcn-the-r-could-stand-for-random","paper_url":"https://arxiv.org/abs/2203.02424v2","paper_title":"R-GCN: The R Could Stand for Random","code":"https://github.com/predict-idlab/RR-GCN","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. 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