{"url":"/dataset/mutag","name":"MUTAG","full_name":null,"description_markdown":"In particular, **MUTAG** is a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium. Input graphs are used to represent chemical compounds, where vertices stand for atoms and are labeled by the atom type (represented by one-hot encoding), while edges between vertices represent bonds between the corresponding atoms. It includes 188 samples of chemical compounds with 7 discrete node labels.\r\n\r\nSource: [Fast and Deep Graph Neural Networks](https://arxiv.org/abs/1911.08941)","description_withheld":null,"homepage":"https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds","first_author":null,"url":"http://pubs.acs.org/doi/abs/10.1021/jm00106a046"},"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":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Explanation Fidelity Evaluation","url":"/task/explanation-fidelity-evaluation","datasets_with_task":"/datasets/task/explanation-fidelity-evaluation"}],"languages":[],"variants":["MUTAG"],"data_loaders":[{"repo":"https://github.com/dmlc/dgl","url":"https://docs.dgl.ai/api/python/dgl.data.html#dgl.data.MUTAGDataset","frameworks":["pytorch","tf","mxnet"]}],"num_papers_in_archive":274,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset_variant":"MUTAG","rows":74,"metrics":["Accuracy","Accuracy (10-fold)","Mean Accuracy","Accuracy (10 fold)"],"first_row_in_archive_order":{"model":"Evolution of Graph Classifiers","paper":"/paper/evolution-of-graph-classifiers","metrics":{"Accuracy":"100.00%","Accuracy (10-fold)":"100"},"code_links":[{"title":"rohand24/GraphCNN_evolution","url":"https://github.com/rohand24/GraphCNN_evolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-mutag","task":"Node Classification","dataset_variant":"MUTAG","rows":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"BoP","paper":"/paper/from-primes-to-paths-enabling-fast-multi","metrics":{"Accuracy":"91.17"},"code_links":[{"title":"kbogas/PAM_BoP","url":"https://github.com/kbogas/PAM_BoP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/explanation-fidelity-evaluation-on-mutag","task":"Explanation Fidelity Evaluation","dataset_variant":"MUTAG","rows":1,"metrics":["fidelity"],"first_row_in_archive_order":{"model":"GCN","paper":"/paper/same-uncovering-gnn-black-box-with-structure","metrics":{"fidelity":"0.702"},"code_links":[{"title":"same2023neurips/same","url":"https://github.com/same2023neurips/same"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/from-primes-to-paths-enabling-fast-multi","title":"From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis","date":"2024-11-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/panda-expanded-width-aware-message-passing","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","date":"2024-06-06","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fine-tuning-graph-neural-networks-by","title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","date":"2023-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":13,"samples_unverified":0,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-level-representation-learning-with","title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","date":"2023-09-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":10,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/same-uncovering-gnn-black-box-with-structure","title":"SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations","date":"2023-09-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-isomorphism-unet","title":"Graph isomorphism UNet","date":"2023-08-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cin-enhancing-topological-message-passing","title":"CIN++: Enhancing Topological Message Passing","date":"2023-06-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scene-reasoning-about-traffic-scenes-using","title":"SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks","date":"2023-01-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cell-attention-networks","title":"Cell Attention Networks","date":"2022-09-16","rows_on_this_dataset":1,"code_links":1,"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/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":3,"code_links":2,"syntology":null},{"paper":"/paper/template-based-graph-neural-network-with","title":"Template based Graph Neural Network with Optimal Transport Distances","date":"2022-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/r-gcn-the-r-could-stand-for-random","title":"R-GCN: The R Could Stand for Random","date":"2022-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dropgnn-random-dropouts-increase-the","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","date":"2021-11-11","rows_on_this_dataset":1,"code_links":1,"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/maximum-entropy-weighted-independent-set","title":"Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks","date":"2021-07-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/relation-order-histograms-as-a-network","title":"Relation order histograms as a network embedding tool","date":"2021-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/accurate-learning-of-graph-representations-1","title":"Accurate Learning of Graph Representations with Graph Multiset Pooling","date":"2021-02-23","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":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/online-graph-dictionary-learning","title":"Online Graph Dictionary Learning","date":"2021-02-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/factorizable-graph-convolutional-networks","title":"Factorizable Graph Convolutional Networks","date":"2020-10-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/wasserstein-embedding-for-graph-learning","title":"Wasserstein Embedding for Graph Learning","date":"2020-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/segmented-graph-bert-for-graph-instance","title":"Segmented Graph-Bert for Graph Instance Modeling","date":"2020-02-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-representation-learning-in-graph","title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","date":"2019-10-24","rows_on_this_dataset":1,"code_links":1,"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/evolution-of-graph-classifiers","title":"Evolution of Graph Classifiers","date":"2019-10-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-universal-self-attention-network","title":"Universal Graph Transformer Self-Attention Networks","date":"2019-09-26","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inducing-a-decision-tree-with-discriminative","title":"Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph","date":"2019-08-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/infograph-unsupervised-and-semi-supervised","title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","date":"2019-07-31","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/isonn-isomorphic-neural-network-for-graph","title":"IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification","date":"2019-07-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-representation-learning-via-hard-and","title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","date":"2019-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improving-attention-mechanism-in-graph-neural","title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation","date":"2019-07-04","rows_on_this_dataset":1,"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/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":1,"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/a-persistent-weisfeilerlehman-procedure-for","title":"A Persistent Weisfeiler–Lehman Procedure for Graph Classification","date":"2019-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wasserstein-weisfeiler-lehman-graph-kernels","title":"Wasserstein Weisfeiler-Lehman Graph Kernels","date":"2019-06-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":0,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/provably-powerful-graph-networks","title":"Provably Powerful Graph Networks","date":"2019-05-27","rows_on_this_dataset":1,"code_links":2,"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/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/function-space-pooling-for-graph","title":"Function Space Pooling For Graph Convolutional Networks","date":"2019-05-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dissecting-graph-neural-networks-on-graph","title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","date":"2019-05-11","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/capsule-graph-neural-network","title":"Capsule Graph Neural Network","date":"2019-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-convolutional-networks-with","title":"Graph Convolutional Networks with EigenPooling","date":"2019-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ddgk-learning-graph-representations-for-deep","title":"DDGK: Learning Graph Representations for Deep Divergence Graph Kernels","date":"2019-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/edgnn-a-simple-and-powerful-gnn-for-directed","title":"edGNN: a Simple and Powerful GNN for Directed Labeled Graphs","date":"2019-04-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/spi-gcn-a-simple-permutation-invariant-graph","title":"SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network","date":"2019-04-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dagcn-dual-attention-graph-convolutional","title":"DAGCN: Dual Attention Graph Convolutional Networks","date":"2019-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rep-the-set-neural-networks-for-learning-set","title":"Rep the Set: Neural Networks for Learning Set Representations","date":"2019-04-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/quantum-based-subgraph-convolutional-neural","title":"Quantum-based subgraph convolutional neural networks","date":"2019-04-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/subgraph-networks-with-application-to","title":"Subgraph Networks with Application to Structural Feature Space Expansion","date":"2019-03-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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/graph-kernels-based-on-linear-patterns","title":"Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons","date":"2019-03-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/capsule-neural-networks-for-graph","title":"Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations","date":"2019-02-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graph-classification-with-recurrent","title":"Variational Recurrent Neural Networks for Graph Classification","date":"2019-02-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/propagation-kernels-efficient-graph-kernels","title":"Propagation kernels: efficient graph kernels from propagated information","date":"2019-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spectral-multigraph-networks-for-discovering","title":"Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules","date":"2018-11-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gaussian-induced-convolution-for-graphs","title":"Gaussian-Induced Convolution for Graphs","date":"2018-11-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-simple-yet-effective-baseline-for-non","title":"A simple yet effective baseline for non-attributed graph classification","date":"2018-11-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/a-simple-baseline-algorithm-for-graph","title":"A Simple Baseline Algorithm for Graph Classification","date":"2018-10-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/weisfeiler-and-leman-go-neural-higher-order","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","date":"2018-10-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/when-work-matters-transforming-classical","title":"When Work Matters: Transforming Classical Network Structures to Graph CNN","date":"2018-07-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/anonymous-walk-embeddings","title":"Anonymous Walk Embeddings","date":"2018-05-30","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/optimal-transport-for-structured-data-with","title":"Optimal Transport for structured data with application on graphs","date":"2018-05-23","rows_on_this_dataset":3,"code_links":2,"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/an-end-to-end-deep-learning-architecture-for","title":"An End-to-End Deep Learning Architecture for Graph Classification","date":"2018-04-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/rdf2vec-rdf-graph-embeddings-and-their","title":"RDF2Vec: RDF Graph Embeddings and Their Applications","date":"2017-11-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph2vec-learning-distributed","title":"graph2vec: Learning Distributed Representations of Graphs","date":"2017-07-17","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dynamic-edge-conditioned-filters-in","title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","date":"2017-04-10","rows_on_this_dataset":1,"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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/modeling-relational-data-with-graph","title":"Modeling Relational Data with Graph Convolutional Networks","date":"2017-03-17","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":32,"samples_ran":10,"samples_unverified":22,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/on-valid-optimal-assignment-kernels-and","title":"On Valid Optimal Assignment Kernels and Applications to Graph Classification","date":"2016-06-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-convolutional-neural-networks-for","title":"Learning Convolutional Neural Networks for Graphs","date":"2016-05-17","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/deep-graph-kernels","title":"Deep Graph Kernels","date":"2015-08-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-metrics-for-persistence-based-2","title":"Learning metrics for persistence-based summaries and applications for graph classification","date":null,"rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":22,"samples_harvested":145,"samples_ran":64,"samples_unverified":81,"pointer_only_for_licence":46,"papers_with_no_sample_that_ran":8,"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."}