{"url":"/dataset/wiki-squirrel","name":"Wiki Squirrel","full_name":"Wikipedia Squirrel","description_markdown":"The data was collected from the English Wikipedia (December 2018). These datasets represent page-page networks on specific topics (chameleons, crocodiles and squirrels). Nodes represent articles and edges are mutual links between them. The edges csv files contain the edges - nodes are indexed from 0. The features json files contain the features of articles - each key is a page id, and node features are given as lists. The presence of a feature in the feature list means that an informative noun appeared in the text of the Wikipedia article. The target csv contains the node identifiers and the average monthly traffic between October 2017 and November 2018 for each page. For each page-page network we listed the number of nodes an edges with some other descriptive statistics.","description_withheld":null,"homepage":"https://github.com/benedekrozemberczki/MUSAE/","introduced_date":"2019-09-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-scale-attributed-node-embedding","title":"Multi-scale Attributed Node Embedding","first_author":"Benedek Rozemberczki","url":null},"license":{"name":"GNU","url":"https://github.com/benedekrozemberczki/MUSAE/"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"},{"name":"Node Clustering","url":"/task/node-clustering","datasets_with_task":"/datasets/task/node-clustering"},{"name":"Node Regression","url":"/task/node-regression","datasets_with_task":"/datasets/task/node-regression"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Squirrel","Wiki Squirrel"],"data_loaders":[],"num_papers_in_archive":208,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-squirrel","task":"Node Classification","dataset_variant":"Squirrel","rows":59,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"FaberNet","paper":"/paper/holonets-spectral-convolutions-do-extend-to","metrics":{"Accuracy":"76.71±1.92"},"code_links":[{"title":"ChristianKoke/HoloNets","url":"https://github.com/ChristianKoke/HoloNets"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/heterophilic-graph-neural-networks","title":"Heterophilic Graph Neural Networks Optimization with Causal Message-passing","date":"2024-11-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/improving-graph-neural-networks-by-learning","title":"Improving Graph Neural Networks by Learning Continuous Edge Directions","date":"2024-10-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/the-heterophilic-snowflake-hypothesis","title":"The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs","date":"2024-06-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-neural-reaction-diffusion-models","title":"Graph Neural Reaction Diffusion Models","date":"2024-06-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transfer-entropy-in-graph-convolutional","title":"Transfer Entropy in Graph Convolutional Neural Networks","date":"2024-06-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sign-is-not-a-remedy-multiset-to-multiset","title":"Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs","date":"2024-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/heterophilous-distribution-propagation-for","title":"Heterophilous Distribution Propagation for Graph Neural Networks","date":"2024-05-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learn-from-heterophily-heterophilous","title":"Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/refining-latent-homophilic-structures-over","title":"Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks","date":"2023-12-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graphrare-reinforcement-learning-enhanced","title":"GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy","date":"2023-12-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cat-a-causally-graph-attention-network-for","title":"CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph","date":"2023-12-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/breaking-the-entanglement-of-homophily-and","title":"Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification","date":"2023-12-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/signgt-signed-attention-based-graph","title":"SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning","date":"2023-10-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/holonets-spectral-convolutions-do-extend-to","title":"HoloNets: Spectral Convolutions do extend to Directed Graphs","date":"2023-10-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}},{"paper":"/paper/make-heterophily-graphs-better-fit-gnn-a","title":"Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach","date":"2022-09-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sheaf-neural-networks-with-connection","title":"Sheaf Neural Networks with Connection Laplacians","date":"2022-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/restructuring-graph-for-higher-homophily-via","title":"Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering","date":"2022-06-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/universal-deep-gnns-rethinking-residual","title":"Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing","date":"2022-05-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/finding-global-homophily-in-graph-neural","title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily","date":"2022-05-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/neural-sheaf-diffusion-a-topological","title":"Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs","date":"2022-02-09","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/understanding-over-squashing-and-bottlenecks-1","title":"Understanding over-squashing and bottlenecks on graphs via curvature","date":"2021-11-29","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cn-motifs-perceptive-graph-neural-networks","title":"CN-Motifs Perceptive Graph Neural Networks","date":"2021-11-15","rows_on_this_dataset":1,"code_links":0,"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":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":7,"samples_unverified":14,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/label-wise-message-passing-graph-neural","title":"Label-Wise Graph Convolutional Network for Heterophilic Graphs","date":"2021-10-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/simple-truncated-svd-based-model-for-node","title":"Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs","date":"2021-06-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/breaking-the-limit-of-graph-neural-networks","title":"Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns","date":"2021-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":1,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improving-graph-neural-networks-with-simple","title":"Improving Graph Neural Networks with Simple Architecture Design","date":"2021-05-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/two-sides-of-the-same-coin-heterophily-and","title":"Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks","date":"2021-02-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/beyond-low-frequency-information-in-graph","title":"Beyond Low-frequency Information in Graph Convolutional Networks","date":"2021-01-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/simple-and-deep-graph-convolutional-networks-1","title":"Simple and Deep Graph Convolutional Networks","date":"2020-07-04","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; 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