# Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence

arXiv: https://arxiv.org/abs/2502.09263 · HTML: https://mlsanity.com/paper/2502.09263/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 91 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 30 of 73)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [A critical look at the evaluation of GNNs under heterophily: Are we really making progress?](https://mlsanity.com/paper/2302.11640/) (2023)
- [Graph Neural Networks with Learnable Structural and Positional Representations](https://mlsanity.com/paper/2110.07875/) (2021)

## Built on by (most cited first, 26 of 26)

- [Turning Tabular Foundation Models into Graph Foundation Models](https://mlsanity.com/paper/2508.20906/) (2025)
- [GraphPFN: A Prior-Data Fitted Graph Foundation Model](https://mlsanity.com/paper/2509.21489/) (2025)
- [Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning](https://mlsanity.com/paper/2602.04998/) (2026)
- [On the Necessity of Learnable Sheaf Laplacians](https://mlsanity.com/paper/2603.05395/) (2026)
- [Fixed Aggregation Features Can Rival GNNs](https://mlsanity.com/paper/2601.19449/) (2026)
- [A Fair Evaluation of Graph Foundation Models for Node Property Prediction](https://mlsanity.com/paper/2606.24509/) (2026)
- [Graph Tokenization for Bridging Graphs and Transformers](https://mlsanity.com/paper/2603.11099/) (2026)
- [Early-Exit Graph Neural Networks](https://mlsanity.com/paper/2505.18088/) (2025)
- [Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework](https://mlsanity.com/paper/2510.18825/) (2025)
- [Node4All: Learning Node Representation Beyond Datasets](https://mlsanity.com/paper/2607.17272/) (2026)
- [Cluster Attention for Graph Machine Learning](https://mlsanity.com/paper/2604.07492/) (2026)
- [End-to-End Subgraph Detection with GraphDETR](https://mlsanity.com/paper/2606.06364/) (2026)
- [Benchmarking Sheaf Neural Networks for Inductive Tasks](https://mlsanity.com/paper/2608.02558/) (2026)
- [Fractal Graph Contrastive Learning](https://mlsanity.com/paper/2505.11356/) (2025)
- [Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation](https://mlsanity.com/paper/2508.01635/) (2025)
- [Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization](https://mlsanity.com/paper/2508.10651/) (2025)
- [Inferring the Graph Structure of Images for Graph Neural Networks](https://mlsanity.com/paper/2509.04677/) (2025)
- [Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study](https://mlsanity.com/paper/2510.18370/) (2025)
- [Enhanced Graph Transformer with Serialized Graph Tokens](https://mlsanity.com/paper/2602.09065/) (2026)
- [Local Message-Passing for Discrete Graph Generation](https://mlsanity.com/paper/2603.08825/) (2026)
- [Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions](https://mlsanity.com/paper/2604.24293/) (2026)
- [Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective](https://mlsanity.com/paper/2606.03290/) (2026)
- [Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus](https://mlsanity.com/paper/2607.28304/) (2026)
- [PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification](https://mlsanity.com/paper/2609.26310/) (2026)
- [Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing](https://mlsanity.com/paper/2610.03556/) (2026)
- [PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification](https://mlsanity.com/paper/2610.15020/) (2026)

## Abstract (arXiv)

Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies. Conversely, Graph Transformers (GTs) are regarded as superior due to their employment of global attention mechanisms, which potentially mitigate these challenges. Literature frequently suggests that GTs outperform GNNs in graph-level tasks, especially for graph classification and regression on small molecular graphs. In this study, we explore the untapped potential of GNNs through an enhanced framework, GNN+, which integrates six widely used techniques: edge feature integration, normalization, dropout, residual connections, feed-forward networks, and positional encoding, to effectively tackle graph-level tasks. We conduct a systematic re-evaluation of three classic GNNs (GCN, GIN, and GatedGCN) enhanced by the GNN+ framework across 14 well-known graph-level datasets. Our results reveal that, contrary to prevailing beliefs, these classic GNNs consistently match or surpass the performance of GTs, securing top-three rankings across all datasets and achieving first place in eight. Furthermore, they demonstrate greater efficiency, running several times faster than GTs on many datasets. This highlights the potential of simple GNN architectures, challenging the notion that complex mechanisms in GTs are essential for superior graph-level performance. Our source code is available at https://github.com/LUOyk1999/GNNPlus.