{"url":"/dataset/citeseer","name":"Citeseer","full_name":null,"description_markdown":"The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words.\r\n\r\nSource: [https://linqs.soe.ucsc.edu/data](https://linqs.soe.ucsc.edu/data)","description_withheld":null,"homepage":"https://linqs.soe.ucsc.edu/data","introduced_date":"1998-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"CiteSeer: An Automatic Citation Indexing System","first_author":null,"url":"https://doi.org/10.1145/276675.276685"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"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":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Node Clustering","url":"/task/node-clustering","datasets_with_task":"/datasets/task/node-clustering"},{"name":"Community Detection","url":"/task/community-detection","datasets_with_task":"/datasets/task/community-detection"},{"name":"Graph Clustering","url":"/task/graph-clustering","datasets_with_task":"/datasets/task/graph-clustering"}],"languages":[],"variants":["Citeseer (weighted evaluation)","Citeseer random partition","Citeseer Full-supervised","Citeseer (nonstandard variant)","Citeseer (biased evaluation)","Citeseer","CiteSeer with Public Split: fixed 5 nodes per class","CiteSeer with Public Split: fixed 20 nodes per class","CiteSeer (1%)","CiteSeer (0.5%)"],"data_loaders":[{"repo":"https://github.com/dmlc/dgl","url":"https://docs.dgl.ai/api/python/dgl.data.html#dgl.data.CiteseerGraphDataset","frameworks":["pytorch","tf","mxnet"]},{"repo":"https://github.com/danielegrattarola/spektral","url":"https://graphneural.network/datasets/#citation","frameworks":["tf"]}],"num_papers_in_archive":381,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-citeseer","task":"Node Classification","dataset_variant":"Citeseer","rows":71,"metrics":["Accuracy","Training Split","Validation","1:1 Accuracy","Accuracy (%)","Inference Time (ms)"],"first_row_in_archive_order":{"model":"ACMII-Snowball-2","paper":"/paper/is-heterophily-a-real-nightmare-for-graph","metrics":{"Accuracy":"82.07 ± 1.04"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-with-public","task":"Node Classification","dataset_variant":"CiteSeer with Public Split: fixed 20 nodes per class","rows":40,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"OGC","paper":"/paper/from-cluster-assumption-to-graph-convolution","metrics":{"Accuracy":"77.5"},"code_links":[{"title":"zhengwang100/ogc_ggcm","url":"https://github.com/zhengwang100/ogc_ggcm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-05","task":"Node Classification","dataset_variant":"CiteSeer (0.5%)","rows":14,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MT-GCN","paper":"/paper/mutual-teaching-for-graph-convolutional","metrics":{"Accuracy":"67.7%"},"code_links":[{"title":"kunzhan/MTGCN","url":"https://github.com/kunzhan/MTGCN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-1","task":"Node Classification","dataset_variant":"CiteSeer (1%)","rows":14,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VCHN","paper":"/paper/view-consistent-heterogeneous-network-on","metrics":{"Accuracy":"70.1%"},"code_links":[{"title":"kunzhan/VCHN","url":"https://github.com/kunzhan/VCHN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-citeseer","task":"Link Prediction","dataset_variant":"Citeseer","rows":13,"metrics":["AUC","AP","Accuracy","ACC"],"first_row_in_archive_order":{"model":"NESS","paper":"/paper/ness-learning-node-embeddings-from-static","metrics":{"AP":"99.5","AUC":"99.43"},"code_links":[{"title":"AstraZeneca/NESS","url":"https://github.com/AstraZeneca/NESS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/graph-clustering-on-citeseer","task":"Graph Clustering","dataset_variant":"Citeseer","rows":9,"metrics":["ACC","NMI","ARI","F1","Precision","F score"],"first_row_in_archive_order":{"model":"R-DGAE","paper":"/paper/rethinking-graph-autoencoder-models-for","metrics":{"ACC":"70.5","ARI":"47.1","NMI":"45.0"},"code_links":[{"title":"nairouz/R-GAE","url":"https://github.com/nairouz/R-GAE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-full","task":"Node Classification","dataset_variant":"Citeseer Full-supervised","rows":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"IncepGCN+DropEdge","paper":"/paper/the-truly-deep-graph-convolutional-networks","metrics":{"Accuracy":"80.50%"},"code_links":[{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT"},{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT/tree/master/graphsaint/open_graph_benchmark"},{"title":"DropEdge/DropEdge","url":"https://github.com/DropEdge/DropEdge"},{"title":"chr26195/pmlp","url":"https://github.com/chr26195/pmlp"},{"title":"zjunet/dropmessage","url":"https://github.com/zjunet/dropmessage"},{"title":"luckytiger123/dropmessage","url":"https://github.com/luckytiger123/dropmessage"},{"title":"sandl99/KGraph","url":"https://github.com/sandl99/KGraph"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-citeseer-biased-evaluation","task":"Link Prediction","dataset_variant":"Citeseer (biased evaluation)","rows":2,"metrics":["AP","AUC","Accuracy"],"first_row_in_archive_order":{"model":"GraphStar (double weight on positive examples)","paper":"/paper/graph-star-net-for-generalized-multi-task-1","metrics":{"AP":"97.93","AUC":"97.47","Accuracy":"97.7"},"code_links":[{"title":"graph-star-team/graph_star","url":"https://github.com/graph-star-team/graph_star"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/community-detection-on-citeseer","task":"Community Detection","dataset_variant":"Citeseer","rows":1,"metrics":["ACC","NMI"],"first_row_in_archive_order":{"model":"CDNMF","paper":"/paper/contrastive-deep-nonnegative-matrix","metrics":{"ACC":"0.4756","NMI":"0.2559"},"code_links":[{"title":"6lyc/cdnmf","url":"https://github.com/6lyc/cdnmf"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/graph-classification-on-citeseer","task":"Graph Classification","dataset_variant":"Citeseer","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"sKNN-LDS","paper":"/paper/neighborhood-enlargement-in-graph-neural","metrics":{"Accuracy":"73.7"},"code_links":[{"title":"CODE-SUBMIT/Graph_Neighborhood_1","url":"https://github.com/CODE-SUBMIT/Graph_Neighborhood_1"},{"title":"CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network","url":"https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-citeseer-nonstandard","task":"Link Prediction","dataset_variant":"Citeseer (nonstandard variant)","rows":1,"metrics":["AP","AUC"],"first_row_in_archive_order":{"model":"GLACE","paper":"/paper/gaussian-embedding-of-large-scale-attributed","metrics":{"AP":"98.37","AUC":"98.43"},"code_links":[{"title":"bhagya-hettige/GLACE","url":"https://github.com/bhagya-hettige/GLACE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-random","task":"Node Classification","dataset_variant":"Citeseer random partition","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GraphMix (GCN)","paper":"/paper/graphmix-regularized-training-of-graph-neural","metrics":{"Accuracy":"76.45 ± 1.57"},"code_links":[{"title":"vikasverma1077/GraphMix","url":"https://github.com/vikasverma1077/GraphMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-citeseer-with-public-1","task":"Node Classification","dataset_variant":"CiteSeer with Public Split: fixed 5 nodes per class","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GraphMix (GCN)","paper":"/paper/graphmix-regularized-training-of-graph-neural","metrics":{"Accuracy":"58.55 ± 2.26"},"code_links":[{"title":"vikasverma1077/GraphMix","url":"https://github.com/vikasverma1077/GraphMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/faster-inference-time-for-gnns-using","title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","date":"2024-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/classic-gnns-are-strong-baselines-reassessing","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","date":"2024-06-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gscan-graph-stability-clustering-for","title":"GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass","date":"2024-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/strong-transitivity-relations-and-graph","title":"Strong Transitivity Relations and Graph Neural Networks","date":"2024-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mitigating-degree-biases-in-message-passing","title":"Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures","date":"2023-12-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/contrastive-deep-nonnegative-matrix","title":"Contrastive Deep Nonnegative Matrix Factorization for Community Detection","date":"2023-11-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/from-cluster-assumption-to-graph-convolution","title":"From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited","date":"2023-09-24","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/transitivity-preserving-graph-representation","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","date":"2023-08-18","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":5,"samples_unverified":21,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-split-matters-flat-minima-methods-for","title":"The Split Matters: Flat Minima Methods for Improving the Performance of GNNs","date":"2023-06-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/graph-entropy-minimization-for-semi","title":"Graph Entropy Minimization for Semi-supervised Node Classification","date":"2023-05-31","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/ness-learning-node-embeddings-from-static","title":"NESS: Node Embeddings from Static SubGraphs","date":"2023-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-mask-aggregators-for-graph-neural","title":"Multi-Mask Aggregators for Graph Neural Networks","date":"2022-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/beyond-homophily-with-graph-echo-state-1","title":"Beyond Homophily with Graph Echo State Networks","date":"2022-10-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/beyond-homophily-structure-aware-path","title":"Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network","date":"2022-07-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-trees-with-attention","title":"TREE-G: Decision Trees Contesting Graph Neural Networks","date":"2022-07-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/diffwire-inductive-graph-rewiring-via-the","title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","date":"2022-06-15","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/inferring-from-references-with-differences","title":"Inferring from References with Differences for Semi-Supervised Node Classification on Graphs","date":"2022-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/how-to-find-your-friendly-neighborhood-graph-1","title":"How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision","date":"2022-04-11","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/view-consistent-heterogeneous-network-on","title":"View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes","date":"2022-03-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/graph-representation-learning-beyond-node-and","title":"Graph Representation Learning Beyond Node and Homophily","date":"2022-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/tackling-oversmoothing-of-gnns-with-1","title":"Deeper-GXX: Deepening Arbitrary GNNs","date":"2021-10-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fdgatii-fast-dynamic-graph-attention-with","title":"FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping","date":"2021-10-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-less-neural-networks-teaching-old-mlps-1","title":"Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation","date":"2021-10-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-link-prediction-with-walk-pooling-1","title":"Neural Link Prediction with Walk Pooling","date":"2021-10-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/is-heterophily-a-real-nightmare-for-graph","title":"Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?","date":"2021-09-12","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/variational-graph-normalized-auto-encoders","title":"Variational Graph Normalized Auto-Encoders","date":"2021-08-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-graph-autoencoder-models-for","title":"Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering","date":"2021-07-19","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/distilling-self-knowledge-from-contrastive","title":"Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages","date":"2021-06-16","rows_on_this_dataset":8,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-bellman-ford-networks-a-general-graph","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","date":"2021-06-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":2,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/extract-the-knowledge-of-graph-neural","title":"Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework","date":"2021-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cleora-a-simple-strong-and-scalable-graph","title":"Cleora: A Simple, Strong and Scalable Graph Embedding Scheme","date":"2021-02-03","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/simple-spectral-graph-convolution","title":"Simple Spectral Graph Convolution","date":"2021-01-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-infoclust-leveraging-cluster-level-node","title":"Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning","date":"2020-09-15","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/snore-scalable-unsupervised-learning-of","title":"SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations","date":"2020-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mutual-teaching-for-graph-convolutional","title":"Mutual Teaching for Graph Convolutional Networks","date":"2020-09-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/optimization-of-graph-neural-networks-with","title":"Optimization of Graph Neural Networks with Natural Gradient Descent","date":"2020-08-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/towards-deeper-graph-neural-networks","title":"Towards Deeper Graph Neural Networks","date":"2020-07-18","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simple-and-deep-graph-convolutional-networks-1","title":"Simple and Deep Graph Convolutional Networks","date":"2020-07-04","rows_on_this_dataset":2,"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; not a correctness claim."}},{"paper":"/paper/nodenet-a-graph-regularised-neural-network","title":"NodeNet: A Graph Regularised Neural Network for Node Classification","date":"2020-06-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/data-augmentation-for-graph-neural-networks","title":"Data Augmentation for Graph Neural Networks","date":"2020-06-11","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-graph-contrastive-representation","title":"Deep Graph Contrastive Representation Learning","date":"2020-06-07","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-random-neural-network","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","date":"2020-05-22","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":8,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bridging-the-gap-between-spectral-and-spatial","title":"Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks","date":"2020-03-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/unifying-graph-convolutional-neural-networks-1","title":"Unifying Graph Convolutional Neural Networks and Label Propagation","date":"2020-02-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":2,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/get-rid-of-suspended-animation-problem-deep","title":"Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification","date":"2020-01-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-bert-only-attention-is-needed-for","title":"Graph-Bert: Only Attention is Needed for Learning Graph Representations","date":"2020-01-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/gaussian-embedding-of-large-scale-attributed","title":"Gaussian Embedding of Large-scale Attributed Graphs","date":"2019-12-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graphair-graph-representation-learning-with","title":"GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction","date":"2019-11-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":7,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/diffusion-improves-graph-learning-1","title":"Diffusion Improves Graph Learning","date":"2019-10-28","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pre-train-and-learn-preserve-global","title":"Pre-train and Learn: Preserve Global Information for Graph Neural Networks","date":"2019-10-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dfnets-spectral-cnns-for-graphs-with-feedback","title":"DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters","date":"2019-10-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-kernel-methods-for-node","title":"Rethinking Kernel Methods for Node Representation Learning on Graphs","date":"2019-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graphmix-regularized-training-of-graph-neural","title":"GraphMix: Improved Training of GNNs for Semi-Supervised Learning","date":"2019-09-25","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/gresnet-graph-residuals-for-reviving-deep","title":"GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation","date":"2019-09-12","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/measuring-and-relieving-the-over-smoothing","title":"Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View","date":"2019-09-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hpgat-high-order-proximity-informed-graph","title":"hpGAT: High-order Proximity Informed Graph Attention Network","date":"2019-08-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adagcn-adaboosting-graph-convolutional","title":"AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models","date":"2019-08-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rwr-gae-random-walk-regularization-for-graph","title":"RWR-GAE: Random Walk Regularization for Graph Auto Encoders","date":"2019-08-12","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-truly-deep-graph-convolutional-networks","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","date":"2019-07-25","rows_on_this_dataset":1,"code_links":7,"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/structure-fusion-based-on-graph-convolutional","title":"Structure fusion based on graph convolutional networks for semi-supervised classification","date":"2019-07-02","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/certifiable-robustness-and-robust-training","title":"Certifiable Robustness and Robust Training for Graph Convolutional Networks","date":"2019-06-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-star-net-for-generalized-multi-task-1","title":"Graph Star Net for Generalized Multi-Task Learning","date":"2019-06-21","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/break-the-ceiling-stronger-multi-scale-deep","title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","date":"2019-06-05","rows_on_this_dataset":11,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/attributed-graph-clustering-via-adaptive","title":"Attributed Graph Clustering via Adaptive Graph Convolution","date":"2019-06-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-flexible-generative-framework-for-graph","title":"A Flexible Generative Framework for Graph-based Semi-supervised Learning","date":"2019-05-26","rows_on_this_dataset":2,"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/neighborhood-enlargement-in-graph-neural","title":"Mutual Information Maximization in Graph Neural Networks","date":"2019-05-21","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-u-nets","title":"Graph U-Nets","date":"2019-05-11","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mixhop-higher-order-graph-convolution","title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","date":"2019-04-30","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/graph-optimized-convolutional-networks","title":"Robust Graph Data Learning via Latent Graph Convolutional Representation","date":"2019-04-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graphnas-graph-neural-architecture-search","title":"GraphNAS: Graph Neural Architecture Search with Reinforcement Learning","date":"2019-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-wavelet-neural-network-1","title":"Graph Wavelet Neural Network","date":"2019-04-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/just-jump-dynamic-neighborhood-aggregation-in","title":"Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks","date":"2019-04-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-discrete-structures-for-graph-neural","title":"Learning Discrete Structures for Graph Neural Networks","date":"2019-03-28","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fast-graph-representation-learning-with","title":"Fast Graph Representation Learning with PyTorch Geometric","date":"2019-03-06","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gated-graph-convolutional-recurrent-neural","title":"Gated Graph Convolutional Recurrent Neural Networks","date":"2019-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-adversarial-training-dynamically","title":"Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure","date":"2019-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-node-classification-via","title":"Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification","date":"2019-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":8,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lanczosnet-multi-scale-deep-graph","title":"LanczosNet: Multi-Scale Deep Graph Convolutional Networks","date":"2019-01-06","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/multi-task-graph-autoencoders","title":"Multi-Task Graph Autoencoders","date":"2018-11-07","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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},{"paper":"/paper/predict-then-propagate-graph-neural-networks","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","date":"2018-10-14","rows_on_this_dataset":2,"code_links":5,"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/encoding-robust-representation-for-graph","title":"Encoding Robust Representation for Graph Generation","date":"2018-09-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-graph-infomax","title":"Deep Graph Infomax","date":"2018-09-27","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":35,"samples_ran":24,"samples_unverified":11,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/every-node-counts-self-ensembling-graph","title":"Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning","date":"2018-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-sampling-towards-fast-graph","title":"Adaptive Sampling Towards Fast Graph Representation Learning","date":"2018-09-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/large-scale-learnable-graph-convolutional","title":"Large-Scale Learnable Graph Convolutional Networks","date":"2018-08-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hyperspherical-variational-auto-encoders","title":"Hyperspherical Variational Auto-Encoders","date":"2018-04-03","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":0,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graphite-iterative-generative-modeling-of","title":"Graphite: Iterative Generative Modeling of Graphs","date":"2018-03-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/n-gcn-multi-scale-graph-convolution-for-semi","title":"N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification","date":"2018-02-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-to-make-predictions-on-graphs-with","title":"Learning to Make Predictions on Graphs with Autoencoders","date":"2018-02-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/adversarially-regularized-graph-autoencoder","title":"Adversarially Regularized Graph Autoencoder for Graph Embedding","date":"2018-02-13","rows_on_this_dataset":3,"code_links":4,"syntology":null},{"paper":"/paper/fastgcn-fast-learning-with-graph","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","date":"2018-01-30","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/splinecnn-fast-geometric-deep-learning-with","title":"SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels","date":"2017-11-24","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","rows_on_this_dataset":4,"code_links":93,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":106,"samples_ran":50,"samples_unverified":56,"pointer_only_for_licence":43,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/watch-your-step-learning-node-embeddings-via","title":"Watch Your Step: Learning Node Embeddings via Graph Attention","date":"2017-10-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/inductive-representation-learning-on-large","title":"Inductive Representation Learning on Large Graphs","date":"2017-06-07","rows_on_this_dataset":4,"code_links":20,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-message-passing-for-quantum-chemistry","title":"Neural Message Passing for Quantum Chemistry","date":"2017-04-04","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/variational-graph-auto-encoders","title":"Variational Graph Auto-Encoders","date":"2016-11-21","rows_on_this_dataset":2,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":58,"samples_ran":31,"samples_unverified":27,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/convolutional-neural-networks-on-graphs-with","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","date":"2016-06-30","rows_on_this_dataset":4,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-semi-supervised-learning-with","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","date":"2016-03-29","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":15,"samples_unverified":13,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gated-graph-sequence-neural-networks","title":"Gated Graph Sequence Neural Networks","date":"2015-11-17","rows_on_this_dataset":2,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/diffusion-convolutional-neural-networks","title":"Diffusion-Convolutional Neural Networks","date":"2015-11-06","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/convolutional-networks-on-graphs-for-learning","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","date":"2015-09-30","rows_on_this_dataset":3,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":0,"samples_unverified":26,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":46,"samples_harvested":543,"samples_ran":204,"samples_unverified":339,"pointer_only_for_licence":116,"papers_with_no_sample_that_ran":15,"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."}