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arxiv: 2501.00773 · v3 · submitted 2025-01-01 · 💻 cs.LG · cs.AI· cs.DB

OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks

Haoyang Li , Yuming Xu , Alexander Zhou , Yongqi Zhang , Jason Chen Zhang , Lei Chen , Qing Li This is my paper

Pith reviewed 2026-05-23 06:08 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.DB
keywords graph neural networksgraph-level tasksbenchmarkmodel selectionexpressivenessrobustnessefficiencygraph topology
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The pith

No single Graph Neural Network architecture dominates both accuracy and efficiency on graph-level tasks across domains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper runs a standardized benchmark called OpenGLT on 20 GNN models across 26 datasets from social, biology, chemistry, and motif domains. It tests classification and regression under clean, noisy, imbalanced, and few-shot conditions. Results show subgraph-based models lead in expressiveness, graph learning and self-supervised methods lead in robustness, and node-based plus pooling models lead in speed. Graph density and centrality offer partial signals for picking the right type for a given dataset. Earlier narrow evaluations left users without reliable ways to match models to needs.

Core claim

Through a unified evaluation framework that groups GNNs into node-based, hierarchical pooling-based, subgraph-based, graph learning-based, and self-supervised learning-based categories, the study finds that no single architecture dominates both effectiveness and efficiency universally. Subgraph-based GNNs excel in expressiveness, graph learning-based and SSL-based methods excel in robustness, and node-based and pooling-based models excel in efficiency. Specific graph topological features such as density and centrality can partially guide the selection of suitable GNN architectures for different graph characteristics.

What carries the argument

The OpenGLT unified evaluation framework that applies a five-category taxonomy of GNNs to standardized tests on graph-level tasks across domains and scenarios.

If this is right

  • Subgraph-based GNNs should be selected when expressiveness is the priority for a graph-level task.
  • Graph learning-based and SSL-based methods should be chosen for datasets that are noisy, imbalanced, or few-shot.
  • Node-based and pooling-based models should be used when computational efficiency matters most.
  • Graph density and centrality metrics can be measured first to narrow architecture choices before training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future benchmarks could test whether hybrids that combine subgraph and pooling components reduce the observed trade-offs.
  • Domain-specific follow-up studies might show stronger or weaker topology guidance in chemistry graphs versus social networks.
  • Practitioners could add a quick topology check step to existing pipelines to improve initial model selection.

Load-bearing premise

The five-category taxonomy covers the main relevant GNN approaches and the 26 datasets plus scenarios are representative enough to support general conclusions about which types to choose.

What would settle it

A GNN that falls outside the five categories but consistently beats all tested models on both accuracy and efficiency metrics across the domains and scenarios, or reversal of the reported performance patterns when the same models are run on additional graphs with different density and centrality values.

Figures

Figures reproduced from arXiv: 2501.00773 by Alexander Zhou, Haoyang Li, Jason Chen Zhang, Lei Chen, Qing Li, Yongqi Zhang, Yuming Xu.

Figure 1
Figure 1. Figure 1: The five types of current GNNs for graph-level tasks. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Framework overview. Lemma 3.2. Given a graph denoted as 𝐺(𝑉 , A, X) and a 𝑘-tuple 𝑡® 𝑘 𝑣𝑖 = (𝑣𝑖 , 𝑣𝑖+1, 𝑣𝑖+2, . . . , 𝑣𝑖+𝑘−1 ) starting from node 𝑣𝑖 , our model can count the number of (𝑘 + 2)-Path between 𝑣𝑖 and any node 𝑣𝑗 ∉ 𝑡® 𝑘 𝑣𝑖 that pass through the path (𝑣𝑖 , 𝑣𝑖+1, 𝑣𝑖+2, . . . , 𝑣𝑖+𝑘−1 ). Proof. Note that since we extract the subgraphs based on the 𝑘 tuple 𝑡® 𝑘 𝑣𝑖 , we can know whether a node belon… view at source ↗
Figure 3
Figure 3. Figure 3: Robustness evaluation. 0.0 0.5 1.0 1.5 2.0 Imbalance ratio β 0.43 0.46 0.49 0.52 0.55 0.58 Accuracy GCN GMT AK+ MO Ours (a) IMDB-M 0.0 0.5 1.0 1.5 2.0 Imbalance ratio β 0.27 0.33 0.39 0.45 0.51 0.57 Accuracy GCN GMT AK+ MO Ours (b) ENZYMES 0.0 0.5 1.0 1.5 2.0 Imbalance ratio β 0.72 0.74 0.76 0.78 0.80 0.82 Accuracy GCN GMT AK+ MO Ours (c) NCI1 0.0 0.5 1.0 1.5 2.0 Imbalance ratio β 0.00 0.15 0.30 0.45 0.60 … view at source ↗
Figure 4
Figure 4. Figure 4: Imbalance data evaluation. 5 10 15 20 25 Few-shot number γ 0.42 0.45 0.48 0.51 0.54 Accuracy GCN GMT AK+ MO Ours (a) IMDB-M 5 10 15 20 25 Few-shot number γ 0.24 0.28 0.32 0.36 0.40 0.44 0.48 Accuracy GCN GMT AK+ MO Ours (b) ENZYMES 5 10 15 20 25 Few-shot number γ 0.55 0.57 0.59 0.61 0.63 0.65 Accuracy GCN GMT AK+ MO Ours (c) NCI1 5 10 15 20 25 Few-shot number γ 0.00 0.15 0.30 0.45 0.60 0.75 MAE GCN GMT I2G… view at source ↗
Figure 5
Figure 5. Figure 5: Few-shot evaluation. the proportions of training samples per class as {1, 1 2 𝛽 , 1 3 𝛽 , . . . , 1 |Y |𝛽 }, where 𝛽 ∈ {0, 0.5, 1, 1.5, 2} controls the imbalance ratio. The num￾ber of samples in the first class is fixed under all 𝛽 values. For graph regression tasks, where 𝑦𝑖 ∈ [𝑦min, 𝑦max], we partition the label range into three equal buckets and vary the proportions of the groups as {1, 1 2 𝛽 , 1 3 𝛽 } … view at source ↗
read the original abstract

Graphs are fundamental data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which involve predicting properties or labels for entire graphs, are crucial for applications like molecular property prediction and subgraph counting. While Graph Neural Networks (GNNs) have shown significant promise for these tasks, their evaluations are often limited by narrow datasets, insufficient architecture coverage, restricted task scope and scenarios, and inconsistent experimental setups, making it difficult to draw reliable conclusions across domains. In this paper, we present a comprehensive experimental study of GNNs on graph-level tasks, systematically categorizing them into five types: node-based, hierarchical pooling-based, subgraph-based, graph learning-based, and self-supervised learning-based GNNs. We propose a unified evaluation framework OpenGLT, which standardizes evaluation across four domains (social networks, biology, chemistry, and motif counting), two task types (classification and regression), and three real-world scenarios (clean, noisy, imbalanced, and few-shot graphs). Extensive experiments on 20 models across 26 classification and regression datasets reveal that: (i) no single architecture dominates both effectiveness and efficiency universally, i.e., subgraph-based GNNs excel in expressiveness, graph learning-based and SSL-based methods in robustness, and node-based and pooling-based models in efficiency; and (ii) specific graph topological features such as density and centrality can partially guide the selection of suitable GNN architectures for different graph characteristics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces the OpenGLT benchmark for graph-level tasks, systematically categorizing GNNs into five types (node-based, hierarchical pooling-based, subgraph-based, graph learning-based, and self-supervised learning-based). It evaluates 20 models across 26 classification and regression datasets spanning four domains (social networks, biology, chemistry, motif counting) and three scenarios (clean, noisy, imbalanced, few-shot), claiming that no single architecture dominates effectiveness and efficiency, with subgraph-based models excelling in expressiveness, graph learning/SSL methods in robustness, node/pooling models in efficiency, and topological features (density, centrality) partially guiding architecture selection.

Significance. If the empirical patterns hold under broader coverage, the work provides actionable guidance for GNN selection in graph-level tasks by quantifying trade-offs across effectiveness, efficiency, and robustness, along with topology-based heuristics. The standardized framework and multi-scenario evaluation are strengths that could reduce inconsistent setups in future studies.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (Taxonomy): The central claim that the five-category taxonomy supports general conclusions on architecture selection (no single model dominates, with category-specific strengths) is load-bearing on the assumption that the taxonomy exhaustively partitions relevant GNN families. However, the manuscript does not explicitly justify exclusion of recent equivariant or higher-order message-passing variants, which could alter the observed expressiveness and robustness rankings if included.
  2. [§5 and Tables 3/4] §5 (Experiments) and Table 3/4 (Results): The reported performance patterns (subgraph-based excelling in expressiveness, etc.) and the topology-guided selection heuristic rely on 26 datasets and 20 models, but the text provides insufficient detail on hyperparameter search ranges, number of random seeds, or statistical tests (e.g., paired t-tests or Wilcoxon) to establish that differences are significant rather than artifacts of the chosen subset.
  3. [§4] §4 (Evaluation Framework): The claim that the 26 datasets across four domains and three scenarios are representative enough for general model-selection advice is undermined without an analysis of topological coverage (e.g., distribution of density and centrality values) or explicit checks that the datasets do not under-sample certain regimes, risking that the observed correlations between topology and preferred architecture are dataset-specific.
minor comments (2)
  1. [§2] §2 (Related Work): Some citations to prior GNN benchmarks appear incomplete; ensure all relevant graph-level surveys (e.g., those covering motif counting) are referenced for context.
  2. [Figure 2] Figure 2 (Taxonomy diagram): The visual categorization would benefit from explicit arrows or labels indicating which models belong to which of the five types to improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Taxonomy): The central claim that the five-category taxonomy supports general conclusions on architecture selection (no single model dominates, with category-specific strengths) is load-bearing on the assumption that the taxonomy exhaustively partitions relevant GNN families. However, the manuscript does not explicitly justify exclusion of recent equivariant or higher-order message-passing variants, which could alter the observed expressiveness and robustness rankings if included.

    Authors: We agree that an explicit justification for the taxonomy boundaries is warranted. The five categories were selected to reflect the dominant architectural paradigms appearing in graph-level literature up to the time of writing; equivariant and higher-order models remain relatively niche for whole-graph tasks and can often be viewed as extensions of node-based or subgraph-based families. In the revision we will add a dedicated paragraph in §3 that (a) states the selection criteria, (b) acknowledges the existence of these variants, and (c) lists their omission as a scope limitation with a forward-looking note. This clarification does not change the reported rankings but improves transparency. revision: yes

  2. Referee: [§5 and Tables 3/4] §5 (Experiments) and Table 3/4 (Results): The reported performance patterns (subgraph-based excelling in expressiveness, etc.) and the topology-guided selection heuristic rely on 26 datasets and 20 models, but the text provides insufficient detail on hyperparameter search ranges, number of random seeds, or statistical tests (e.g., paired t-tests or Wilcoxon) to establish that differences are significant rather than artifacts of the chosen subset.

    Authors: We accept that additional experimental detail is required. The original runs used a fixed grid search whose ranges are only summarized; we will expand §5 to list the exact hyper-parameter ranges, confirm that five random seeds were used throughout, and insert Wilcoxon signed-rank tests (with p-values) for all pairwise comparisons that underpin the category-level conclusions. These additions will be placed in the main text and supplementary material. revision: yes

  3. Referee: [§4] §4 (Evaluation Framework): The claim that the 26 datasets across four domains and three scenarios are representative enough for general model-selection advice is undermined without an analysis of topological coverage (e.g., distribution of density and centrality values) or explicit checks that the datasets do not under-sample certain regimes, risking that the observed correlations between topology and preferred architecture are dataset-specific.

    Authors: This observation is fair. While the 26 datasets were chosen for domain diversity, we did not quantify their coverage of topological regimes. In the revised §4 we will add (i) histograms and summary statistics of density, average degree, and betweenness centrality across all datasets, and (ii) a short discussion of potential under-represented regimes together with the corresponding limitation statement. These additions will allow readers to assess the scope of the topology-guided heuristics directly. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark with no derivation chain or self-referential steps

full rationale

The paper's central claims rest entirely on direct empirical comparisons of 20 models across 26 datasets in four domains and multiple scenarios (clean, noisy, imbalanced, few-shot). No mathematical derivations, equations, fitted parameters renamed as predictions, or self-citation chains are invoked to support the taxonomy or performance patterns. The five-category taxonomy is presented as an organizational framework for the benchmark rather than a self-definitional or uniqueness-derived result. All conclusions are falsifiable via the reported experiments and do not reduce to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The benchmark conclusions depend on domain assumptions about category coverage and dataset representativeness rather than any fitted numerical parameters or newly postulated entities.

axioms (2)
  • domain assumption The five categories (node-based, hierarchical pooling-based, subgraph-based, graph learning-based, and self-supervised learning-based) comprehensively cover GNN approaches for graph-level tasks.
    The paper structures its entire study around this taxonomy without evidence that the categories are exhaustive.
  • domain assumption The 26 datasets across social networks, biology, chemistry, and motif counting plus the clean/noisy/imbalanced/few-shot scenarios are representative of real-world graph-level tasks.
    General claims about architecture suitability rest on these choices being broadly applicable.

pith-pipeline@v0.9.0 · 5817 in / 1479 out tokens · 54591 ms · 2026-05-23T06:08:07.361957+00:00 · methodology

discussion (0)

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