Graph Neural Network

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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.

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

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

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

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 1,516 papers with code (3,598 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.

• SuperGlue: Learning Feature Matching with Graph Neural Networks
  26 Nov 2019 19 repositories listed Syntology ran 6 of 22 samples · 16 unverified · 6 pointer-only (licence)
  This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
• Hierarchical Graph Representation Learning with Differentiable Pooling
  22 Jun 2018 14 repositories listed Syntology ran 1 of 20 samples · 19 unverified
  Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node…
• GNNExplainer: Generating Explanations for Graph Neural Networks
  10 Mar 2019 12 repositories listed Syntology ran 1 of 22 samples · 21 unverified
  We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
• Inductive Relation Prediction by Subgraph Reasoning
  16 Nov 2019 10 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.
• Link Prediction Based on Graph Neural Networks
  27 Feb 2018 10 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs.
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
  31 May 2019 9 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
• Graph Neural Networks for Social Recommendation
  19 Feb 2019 9 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 3 pointer-only (licence)
  These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of…
• KGAT: Knowledge Graph Attention Network for Recommendation
  20 May 2019 7 repositories listed Syntology ran 7 of 13 samples · 6 unverified · 1 pointer-only (licence)
  To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
• DeepSphere: towards an equivariant graph-based spherical CNN
  8 Apr 2019 7 repositories listed Syntology ran 1 of 12 samples · 11 unverified
  Spherical data is found in many applications.
• Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
  16 Feb 2021 6 repositories listed
  Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
  19 Aug 2020 6 repositories listed Syntology ran 1 of 17 samples · 16 unverified
  Finally, the selected neighbors across different relations are aggregated together.
• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
  11 May 2019 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
• Few-Shot Learning with Graph Neural Networks
  10 Nov 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
• The Open Catalyst 2020 (OC20) Dataset and Community Challenges
  20 Oct 2020 5 repositories listed Syntology ran 0 of 11 samples · 11 unverified · 11 pointer-only (licence)
  Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.
• A Fair Comparison of Graph Neural Networks for Graph Classification
  20 Dec 2019 5 repositories listed
  We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
• CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
  16 Aug 2019 5 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
  18 Jul 2019 5 repositories listed
  Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
• Graph Neural Networks: A Review of Methods and Applications
  20 Dec 2018 5 repositories listed
  Lots of learning tasks require dealing with graph data which contains rich relation information among elements.
• Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches
  23 Feb 2023 4 repositories listed
  Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency,…
• Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
  23 Jun 2022 4 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like…
• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
  15 May 2022 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.
• Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
  1 Apr 2021 4 repositories listed
  Scene graph generation is an important visual understanding task with a broad range of vision applications.
• Hierarchical Graph Representations in Digital Pathology
  22 Feb 2021 4 repositories listed
  We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level…
• Graph Neural Network for Traffic Forecasting: A Survey
  27 Jan 2021 4 repositories listed
  In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of…
• Parameterized Explainer for Graph Neural Network
  9 Nov 2020 4 repositories listed Syntology ran 3 of 8 samples · 5 unverified · 8 pointer-only (licence)
  The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the…
• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
  23 Oct 2020 4 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
• Detecting Beneficial Feature Interactions for Recommender Systems
  2 Aug 2020 4 repositories listed
  To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial…
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
  20 Jun 2020 4 repositories listed Syntology ran 13 of 21 samples · 8 unverified · 6 pointer-only (licence)
  We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
  17 Jun 2020 4 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Graph representation learning has emerged as a powerful technique for addressing real-world problems.
• Multipole Graph Neural Operator for Parametric Partial Differential Equations
  16 Jun 2020 4 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural…

Syntology lines on 21 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.