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MUTAG Benchmark (Graph Classification)
Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
The archive carries no text for this table; the description above is the archive's text for the task Graph Classification. archive 2025-07-28
Over time archive 2025-07-28
The chart needs JavaScript; the table below carries every value.
Direction inferred from the metric name, not from the archive: Accuracy (higher is better), Accuracy (10-fold) (higher is better), Mean Accuracy (higher is better), Accuracy (10 fold) (higher is better). Points are placed at the row's paper date; 74 of 74 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Evolution of Graph Classifiers | 100.00% | 100 | – | Paper | Code | 2019 | linked, not harvested | report | ||
| 2 | MEWISPool | 96.66% | – | Paper | Code | 2021 | 2 of 2 ran · 0 unverified | report | |||
| 3 | TFGW ADJ (L=2) | 96.4% | – | Paper | Code | 2022 | linked, not harvested | report | |||
| 4 | GIUNet | 95.7% | – | Paper | Code | 2023 | linked, not harvested | report | |||
| 5 | G_Inception | 95.00% | – | Paper | – | 2018 | no code linked | report | |||
| 6 | GIC | 94.44% | – | Paper | – | 2018 | no code linked | report | |||
| 7 | CIN++ | 94.4% | – | Paper | Code | 2023 | linked, not harvested | report | |||
| 8 | sGIN | 94.14% | ✓ | Paper | Code | 2019 | linked, not harvested | report | |||
| 9 | CAN | 94.1% | – | Paper | Code | 2022 | 0 of 5 ran · 5 unverified | report | |||
| 10 | Deep WL SGN(0,1,2) | 93.68% | – | Paper | – | 2019 | no code linked | report | |||
| 11 | QS-CNNs (Quantum Walk) | 93.13% | 93.13 | – | Paper | – | 2019 | no code linked | report | ||
| 12 | PATCHY-SAN | 92.63% | – | Paper | Code | 2016 | linked, not harvested | report | |||
| 13 | DDGK | 91.58% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 14 | Graph-JEPA | 91.25% | – | Paper | Code | 2023 | 10 of 11 ran · 1 unverified | report | |||
| 15 | GraphStar | 91.2% | – | Paper | Code | 2019 | 1 of 6 ran · 5 unverified | report | |||
| 16 | TREE-G | 91.1% | – | Paper | Code | 2022 | 0 of 10 ran · 10 unverified | report | |||
| 17 | SEG-BERT | 90.85% | – | Paper | Code | 2020 | linked, not harvested | report | |||
| 18 | GFN | 90.84% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 19 | PPGN | 90.55% | – | Paper | Code | 2019 | 0 of 8 ran · 8 unverified | report | |||
| 20 | GAT-GC (f-Scaled) | 90.44% | ✓ | Paper | Code | 2019 | 0 of 1 ran · 1 unverified | report | |||
| 21 | DropGIN | 90.4% | – | Paper | Code | 2021 | 0 of 3 ran · 3 unverified | report | |||
| 22 | LDP | 90.1% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 23 | R-GCN + PANDA | 90.05% | – | Paper | Code | 2024 | 7 of 9 ran · 2 unverified | report | |||
| 24 | hGANet | 90.00% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 25 | U2GNN | 89.97% | – | Paper | Code | 2019 | 4 of 4 ran · 0 unverified | report | |||
| 26 | FactorGCN | 89.9% | 89.9% | – | Paper | Code | 2020 | 2 of 2 ran · 0 unverified | report | ||
| 27 | GFN-light | 89.89% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 28 | GIN-0 | 89.4% | ✓ | Paper | Code | 2018 | 3 of 10 ran · 7 unverified | report | |||
| 29 | Multigraph ChebNet | 89.1% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 30 | InfoGraph | 89.01% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 31 | PSCN | 88.95% | – | Paper | Code | 2016 | linked, not harvested | report | |||
| 32 | BC + Capsules | 88.9% | – | Paper | – | 2019 | no code linked | report | |||
| 33 | edGNN (max) | 88.8% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 34 | GIN + PANDA | 88.75% | – | Paper | Code | 2024 | 7 of 9 ran · 2 unverified | report | |||
| 35 | NERO | 88.68% | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 36 | Path up to length h | 88.47% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 37 | U2GNN (Unsupervised) | 88.47% | – | Paper | Code | 2019 | 4 of 4 ran · 0 unverified | report | |||
| 38 | FGW wl h=4 sp | 88.42% | – | Paper | Code | 2018 | 3 of 3 ran · 0 unverified | report | |||
| 39 | SF + RFC | 88.4% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 40 | ECC (5 scores) | 88.33% | – | Paper | Code | 2017 | 0 of 2 ran · 2 unverified | report | |||
| 41 | WEGL | 88.3% | – | Paper | Code | 2020 | linked, not harvested | report | |||
| 42 | R-GIN + PANDA | 88.2% | – | Paper | Code | 2024 | 7 of 9 ran · 2 unverified | report | |||
| 43 | AWE | 87.87% | – | Paper | Code | 2018 | 2 of 3 ran · 1 unverified | report | |||
| 44 | Graphlet Kernel | 87.7% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 45 | CT-Layer | 87.58% | – | Paper | Code | 2022 | linked, not harvested | report | |||
| 46 | WKPI-kcenters | 87.5% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 47 | DGK | 87.44% | – | Paper | – | 2015 | no code linked | report | |||
| 48 | WWL | 87.27% | – | Paper | Code | 2019 | 0 of 11 ran · 11 unverified | report | |||
| 49 | DAGCN | 87.22% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 50 | GDL-g (SP) | 87.09% | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 51 | edGNN (avg) | 86.9% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 52 | GAP-Layer (Rcut) | 86.9% | – | Paper | Code | 2022 | linked, not harvested | report | |||
| 53 | GAP-Layer (Ncut) | 86.9% | – | Paper | Code | 2022 | linked, not harvested | report | |||
| 54 | CapsGNN | 86.67% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 55 | FGW wl h=2 sp | 86.42% | – | Paper | Code | 2018 | 3 of 3 ran · 0 unverified | report | |||
| 56 | ApproxRepSet | 86.33% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 57 | VRGC | 86.3% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 58 | k-GNN | 86.1% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 59 | DGCNN | 85.83% | – | Paper | Code | 2018 | linked, not harvested | report | |||
| 60 | GCN + PANDA | 85.75% | ✓ | Paper | Code | 2024 | 7 of 9 ran · 2 unverified | report | |||
| 61 | GIN-0 | 85.7% | ✓ | Paper | Code | 2019 | 0 of 2 ran · 2 unverified | report | |||
| 62 | NDP | 84.7% | – | Paper | Code | 2019 | 1 of 1 ran · 0 unverified | report | |||
| 63 | Propagation kernels (pk) | 84.5% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 64 | WL-OA | 84.5% | – | Paper | – | 2016 | no code linked | report | |||
| 65 | SPI-GCN | 84.40% | – | Paper | – | 2019 | no code linked | report | |||
| 66 | GMT | 83.44% | – | Paper | Code | 2021 | 4 of 5 ran · 1 unverified | report | |||
| 67 | Function Space Pooling | 83.3% | – | Paper | – | 2019 | no code linked | report | |||
| 68 | Function Space Pooling | 83.3% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 69 | FGW raw sp | 83.26% | – | Paper | Code | 2018 | 3 of 3 ran · 0 unverified | report | |||
| 70 | graph2vec | 83.15% ± 9.25% | – | Paper | Code | 2017 | 2 of 2 ran · 0 unverified | report | |||
| 71 | EigenGCN-3 | 79.5% | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 72 | GDL-g (ADJ) | 58.45% | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 73 | P-WL-C | 90.51 | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 74 | G-Tuning | 86.14 | – | Paper | Code | 2023 | 13 of 13 ran · 0 unverified | report |
All 74 rows shown. 74 link to a paper page on this site; 5 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 27 rows have a graph line, from 21 distinct papers; 19 rows (13 papers) have at least one sample that ran. Counting each paper once: Syntology ran 54 of 113 samples; 59 unverified. Separately, 31 of those 113 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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