Graph Regression

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

23 leaderboard tables shown for this task, 23 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 23 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

21 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 118 papers with code (145 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Graph Attention Networks
  30 Oct 2017 93 repositories listed Syntology ran 50 of 106 samples · 56 unverified · 43 pointer-only (licence)
  We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph…
• Semi-Supervised Classification with Graph Convolutional Networks
  9 Sep 2016 55 repositories listed Syntology ran 31 of 58 samples · 27 unverified · 22 pointer-only (licence)
  We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
• Inductive Representation Learning on Large Graphs
  7 Jun 2017 20 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
• Neural Message Passing for Quantum Chemistry
  4 Apr 2017 20 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
• How Powerful are Graph Neural Networks?
  1 Oct 2018 19 repositories listed Syntology ran 3 of 10 samples · 7 unverified · 5 pointer-only (licence)
  Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
• Benchmarking Graph Neural Networks
  2 Mar 2020 15 repositories listed Syntology ran 1 of 23 samples · 22 unverified
  In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
• How Attentive are Graph Attention Networks?
  30 May 2021 8 repositories listed Syntology ran 6 of 16 samples · 10 unverified · 1 pointer-only (licence)
  Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
• Principal Neighbourhood Aggregation for Graph Nets
  12 Apr 2020 8 repositories listed Syntology ran 33 of 55 samples · 22 unverified · 48 pointer-only (licence)
  Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
• Convolutional Networks on Graphs for Learning Molecular Fingerprints
  30 Sep 2015 8 repositories listed Syntology ran 0 of 26 samples · 26 unverified · 1 pointer-only (licence)
  We introduce a convolutional neural network that operates directly on graphs.
• Simplifying Graph Convolutional Networks
  19 Feb 2019 7 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
  17 Mar 2021 6 repositories listed
  Enabling effective and efficient machine learning (ML) over large-scale graph data (e.
• Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
  9 Mar 2022 5 repositories listed Syntology ran 4 of 9 samples · 5 unverified
  This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
• Do Transformers Really Perform Bad for Graph Representation?
  9 Jun 2021 5 repositories listed
  Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
• Molecular Fingerprints Are Strong Models for Peptide Function Prediction
  29 Jan 2025 4 repositories listed
  We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive…
• Recipe for a General, Powerful, Scalable Graph Transformer
  25 May 2022 4 repositories listed Syntology ran 3 of 21 samples · 18 unverified
  We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
• Simple and Deep Graph Convolutional Networks
  4 Jul 2020 4 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 4 pointer-only (licence)
  We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
• Geometric deep learning on graphs and manifolds using mixture model CNNs
  25 Nov 2016 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications…
• Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
  7 Feb 2024 3 repositories listed Syntology ran 10 of 13 samples · 3 unverified
  We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
• Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
  28 Oct 2023 3 repositories listed Syntology ran 11 of 12 samples · 1 unverified
  However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its…
• A Generalization of ViT/MLP-Mixer to Graphs
  27 Dec 2022 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.
• MEGAN: Multi-Explanation Graph Attention Network
  23 Nov 2022 3 repositories listed
  Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
• Global Self-Attention as a Replacement for Graph Convolution
  7 Aug 2021 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on…
• A Generalization of Transformer Networks to Graphs
  17 Dec 2020 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs.
• Graph Neural Networks for Jamming Source Localization
  1 Jun 2025 2 repositories listed
  Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored.
• GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
  24 Apr 2025 2 repositories listed
  Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships…
• Beyond Message Passing: Neural Graph Pattern Machine
  30 Jan 2025 2 repositories listed Syntology ran 6 of 11 samples · 5 unverified
  Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance.
• A Canonicalization Perspective on Invariant and Equivariant Learning
  28 May 2024 2 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 1 pointer-only (licence)
  In this work, we introduce a canonicalization perspective that provides an essential and complete view of the design of frames.
• Topology-Informed Graph Transformer
  3 Feb 2024 2 repositories listed Syntology ran 14 of 21 samples · 7 unverified · 21 pointer-only (licence)
  Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs).
• Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
  1 Sep 2023 2 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al.
• Graph Inductive Biases in Transformers without Message Passing
  27 May 2023 2 repositories listed Syntology ran 6 of 12 samples · 6 unverified
  Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.

Syntology lines on 24 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.