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Node Classification archive 2025-07-28

Reddit Benchmark (Node Classification)

16 rows 14 with code listed 2 metrics Dataset page

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

The archive carries no text for this table; the description above is the archive's text for the task Node 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), Micro-F1 (higher is better). Points are placed at the row's paper date; 16 of 16 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 BNS-GCN 97.17% – Paper Code 2022 linked, not harvested report
2 CoFree-GNN 97.14±0.02% – Paper – 2023 no code linked report
3 shaDow-GAT 97.13% – Paper Code 2022 0 of 2 ran · 2 unverified report
4 shaDow-SAGE 97.03% – Paper Code 2022 0 of 2 ran · 2 unverified report
5 JKNet+DropEdge 97.02% – Paper Code 2019 0 of 5 ran · 5 unverified report
6 GraphSAINT 97.0% – Paper Code 2019 linked, not harvested report
7 EnGCN 96.65% – Paper Code 2022 4 of 6 ran · 2 unverified report
8 SIGN 96.60% – Paper Code 2020 2 of 7 ran · 5 unverified report
9 ASGCN 96.27% – Paper Code 2018 linked, not harvested report
10 PCAPass + XGBoost 96.26 ± 0.02% – Paper Code 2022 linked, not harvested report
11 SSGC 95.3 – Paper Code 2021 linked, not harvested report
12 VQ-GNN (SAGE-Mean) 94.5 ± .0024 – Paper Code 2021 linked, not harvested report
13 GraphSAGE 94.32% – Paper Code 2017 3 of 5 ran · 2 unverified report
14 FastGCN 93.70% – Paper Code 2018 0 of 3 ran · 3 unverified report
15 TGCL+ResNet 81.06±1.18% – Paper – 2021 no code linked report
16 GRACE 94.2 ± 0.0 – Paper Code 2020 1 of 1 ran · 0 unverified report

All 16 rows shown. 16 link to a paper page on this site; 0 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). 8 rows have a graph line, from 7 distinct papers; 4 rows (4 papers) have at least one sample that ran. Counting each paper once: Syntology ran 10 of 29 samples; 19 unverified. Separately, 11 of those 29 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections