Papers › Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

1 Sep 2023arXiv:2309.00367archive 2025-07-28

Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, Martin Grohe

The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Graph Transformers significantly outperform Message Passing GNNs (MPGNNs). In this paper, we carefully reevaluate multiple MPGNN baselines as well as the Graph Transformer GPS (Ramp\'a\v{s}ek et al. 2022) on LRGB. Through a rigorous empirical analysis, we demonstrate that the reported performance gap is overestimated due to suboptimal hyperparameter choices. It is noteworthy that across multiple datasets the performance gap completely vanishes after basic hyperparameter optimization. In addition, we discuss the impact of lacking feature normalization for LRGB's vision datasets and highlight a spurious implementation of LRGB's link prediction metric. The principal aim of our paper is to establish a higher standard of empirical rigor within the graph machine learning community.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2309.00367")

Code

Syntology Ran 5 of 12 code samples harvested from 2 repositories linked to this paper; 7 have no recorded run. Of those that ran: 2 ran · violated contract; 3 ran with no contract checked.

By repository: official repository: 10 samples from 1 repository, 4 ran; community (archive-listed): 2 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

toenshoff/lrgb officialmentioned in paperpytorchMIT report
Fedzbar/laser-release mentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

12 samples harvested; 5 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · violated contract
3ran
7unverified

Licence: 0 of the 12 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

default toenshoff/lrgb/graphgps/layer/performer_layer.py official repository ran · violated contract fingerprinted MIT (permissive) · 60fff7c3c400d7ff · report
empty toenshoff/lrgb/graphgps/layer/performer_layer.py official repository ran fingerprinted MIT (permissive) · 19925529d217754e · report
exists toenshoff/lrgb/graphgps/layer/performer_layer.py official repository ran · violated contract MIT (permissive) · aa5486a3650902d8 · report
get_final_pretrained_ckpt toenshoff/lrgb/graphgps/finetuning.py official repository ran MIT (permissive) · 1bb331bf70a78136 · report
apply_chunking_to_forward toenshoff/lrgb/graphgps/layer/bigbird_layer.py official repository unverified MIT (permissive) · ae56bcfaae96da3c · report
init_model_from_pretrained toenshoff/lrgb/graphgps/finetuning.py official repository unverified MIT (permissive) · 3db4474abe13fdee · report
is_seed toenshoff/lrgb/graphgps/agg_runs.py official repository unverified MIT (permissive) · c98a702be0660ac9 · report
is_split toenshoff/lrgb/graphgps/agg_runs.py official repository unverified MIT (permissive) · 438635c08adfe2aa · report
join_list toenshoff/lrgb/graphgps/agg_runs.py official repository unverified MIT (permissive) · 40e98e1ecbf39f34 · report
load_pretrained_model_cfg toenshoff/lrgb/graphgps/finetuning.py official repository unverified MIT (permissive) · d796cdea5889c847 · report
init_model_from_pretrained Fedzbar/laser-release/laser/finetuning.py community (archive-listed) ran MIT (permissive) · 8653a762bb268420 · report
load_pretrained_model_cfg Fedzbar/laser-release/laser/finetuning.py community (archive-listed) unverified MIT (permissive) · 995500eb98f90144 · report

Tasks

Graph ClassificationGraph LearningGraph RegressionHyperparameter OptimizationLink PredictionNode Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Classification Peptides-func GCN-tuned AP 0.6860±0.0050 #20 of 44 Archive leaderboard report
Graph Classification Peptides-func GatedGCN-tuned AP 0.6765±0.0047 #23 of 44 Archive leaderboard report
Graph Classification Peptides-func GINE-tuned AP 0.6621±0.0067 #27 of 44 Archive leaderboard report
Graph Classification Peptides-func GPS-tuned AP 0.6534±0.0091 #31 of 44 Archive leaderboard report
Graph Regression Peptides-struct GCN-tuned MAE 0.2460±0.0007 #11 of 39 Archive leaderboard report
Graph Regression Peptides-struct GINE-tuned MAE 0.2473±0.0017 #14 of 39 Archive leaderboard report
Graph Regression Peptides-struct GatedGCN-tuned MAE 0.2477±0.0009 #16 of 39 Archive leaderboard report
Graph Regression Peptides-struct GPS-tuned MAE 0.2509±0.0014 #24 of 39 Archive leaderboard report
Link Prediction PCQM-Contact GINE-tuned MRR 0.3509±0.0006 #12 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GINE-tuned MRR-ext-filtered 0.4617±0.0005 #12 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GPS-tuned MRR 0.3498±0.0005 #13 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GPS-tuned MRR-ext-filtered 0.4703±0.0014 #13 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GatedGCN-tuned MRR 0.3495±0.0010 #14 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GatedGCN-tuned MRR-ext-filtered 0.4670±0.0004 #14 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GCN-tuned MRR 0.3424±0.0007 #16 of 18 Archive leaderboard report
Link Prediction PCQM-Contact GCN-tuned MRR-ext-filtered 0.4526±0.0006 #16 of 18 Archive leaderboard report
Node Classification COCO-SP GPS-tuned macro F1 0.3884±0.0055 #2 of 19 Archive leaderboard report
Node Classification COCO-SP GatedGCN-tuned macro F1 0.2922±0.0018 #8 of 19 Archive leaderboard report
Node Classification COCO-SP GINE-tuned macro F1 0.2125±0.0009 #14 of 19 Archive leaderboard report
Node Classification COCO-SP GCN-tuned macro F1 0.1338±0.0007 #18 of 19 Archive leaderboard report
Node Classification PascalVOC-SP GPS-tuned macro F1 0.4440±0.0065 #3 of 21 Archive leaderboard report
Node Classification PascalVOC-SP GatedGCN-tuned macro F1 0.3880±0.0040 #7 of 21 Archive leaderboard report
Node Classification PascalVOC-SP GINE-tuned macro F1 0.2718±0.0054 #16 of 21 Archive leaderboard report
Node Classification PascalVOC-SP GCN-tuned macro F1 0.2078±0.0031 #18 of 21 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutGPSGraph TransformerLabel SmoothingLapEigenLaplacian PELayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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