{"url":"/dataset/wiki","name":"Wiki","full_name":"Web Traffic Time Series Forecasting","description_markdown":"### Context\r\n\r\nThere's a story behind every dataset and here's your opportunity to share yours.\r\n\r\n### Content\r\n\r\nWhat's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.\r\n\r\n### Acknowledgements\r\n\r\nWe wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.\r\n\r\n### Inspiration\r\n\r\nYour data will be in front of the world's largest data science community. What questions do you want to see answered?","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/ymlai87416/wiktraffictimeseriesforecast","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"CC0: Public Domain","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"},{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Node Clustering","url":"/task/node-clustering","datasets_with_task":"/datasets/task/node-clustering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Wiki"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/link-prediction-on-wiki","task":"Link Prediction","dataset_variant":"Wiki","rows":2,"metrics":["AUC"],"first_row_in_archive_order":{"model":"","paper":"/paper/an-effective-graph-learning-based-approach","metrics":{"AUC":"200%"},"code_links":[{"title":"im0qianqian/WSDM2022TGP-AntGraph","url":"https://github.com/im0qianqian/WSDM2022TGP-AntGraph"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-wiki","task":"Node Classification","dataset_variant":"Wiki","rows":2,"metrics":["AUC","Macro F1","Micro F1"],"first_row_in_archive_order":{"model":"DANMF","paper":"/paper/deep-autoencoder-like-nonnegative-matrix","metrics":{"AUC":"41.12%"},"code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"benedekrozemberczki/DANMF","url":"https://github.com/benedekrozemberczki/DANMF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-wiki-1","task":"Node Classification","dataset_variant":"wiki","rows":2,"metrics":["ACCURACY"],"first_row_in_archive_order":{"model":"A2DUG","paper":"/paper/why-using-either-aggregated-features-or","metrics":{"ACCURACY":"65.13±0.07"},"code_links":[{"title":"seijimaekawa/a2dug","url":"https://github.com/seijimaekawa/a2dug"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/why-using-either-aggregated-features-or","title":"A Simple and Scalable Graph Neural Network for Large Directed Graphs","date":"2023-06-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":13,"samples_ran":3,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-effective-graph-learning-based-approach","title":"An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022","date":"2022-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/large-scale-learning-on-non-homophilous","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","date":"2021-10-27","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":21,"samples_ran":11,"samples_unverified":10,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bridging-the-gap-between-community-and-node","title":"Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection","date":"2019-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-autoencoder-like-nonnegative-matrix","title":"Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection","date":"2018-10-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/binarized-attributed-network-embedding","title":"Binarized Attributed Network Embedding","date":"2018-10-22","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":34,"samples_ran":14,"samples_unverified":20,"pointer_only_for_licence":4,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}