{"url":"/sota/node-classification-on-dblp","task":{"name":"Node Classification","url":"/task/node-classification","note":null},"dataset":{"name":"DBLP","url":"/dataset/dblp"},"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","Micro F1","Inference Time (ms)","Macro F1"],"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","Micro F1":"higher","Inference Time (ms)":"lower","Macro F1":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GRACE","metrics":{"Accuracy":"84.2 ± 0.1"},"uses_additional_data":false,"paper_date":"2020-06-07","paper":"/paper/deep-graph-contrastive-representation","paper_url":"https://arxiv.org/abs/2006.04131v2","paper_title":"Deep Graph Contrastive Representation Learning","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/grace","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":2,"model":"RR-GCN-PPV","metrics":{"Accuracy":"70.61"},"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":"R-GCN","metrics":{"Accuracy":"68.51"},"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":4,"model":"DAOR","metrics":{"Macro F1":"87.64","Micro F1":"87.86"},"uses_additional_data":false,"paper_date":"2019-12-17","paper":"/paper/bridging-the-gap-between-community-and-node","paper_url":"https://arxiv.org/abs/1912.08808v1","paper_title":"Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection","code":"https://github.com/eXascaleInfolab/daor","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"PairE","metrics":{"Micro F1":"80.58"},"uses_additional_data":false,"paper_date":"2022-03-03","paper":"/paper/graph-representation-learning-beyond-node-and","paper_url":"https://arxiv.org/abs/2203.01564v1","paper_title":"Graph Representation Learning Beyond Node and Homophily","code":"https://github.com/syvail/PairE-Graph-Representation-Learning-Beyond-Node-and-Homophily","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"FIT-GNN","metrics":{"Inference Time (ms)":"0.0018"},"uses_additional_data":false,"paper_date":"2024-10-19","paper":"/paper/faster-inference-time-for-gnns-using","paper_url":"https://arxiv.org/abs/2410.15001v2","paper_title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","code":"https://github.com/Roy-Shubhajit/FIT-GNN","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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