Graph Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

73 leaderboard tables shown for this task, 73 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 73 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

54 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 54 until expanded.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 483 papers with code (927 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.
• Modeling Relational Data with Graph Convolutional Networks
  17 Mar 2017 27 repositories listed Syntology ran 10 of 32 samples · 22 unverified · 15 pointer-only (licence)
  We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
• ImageNet Classification with Deep Convolutional Neural Networks
  1 Dec 2012 23 repositories listed
  We trained a large, deep convolutional neural network to classify the 1.
• 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.
• 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.
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
  6 Feb 2020 18 repositories listed Syntology ran 2 of 7 samples · 5 unverified · 2 pointer-only (licence)
  We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
• 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.
• 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…
• Gated Graph Sequence Neural Networks
  17 Nov 2015 13 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
• 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.
• Strategies for Pre-training Graph Neural Networks
  29 May 2019 11 repositories listed Syntology ran 10 of 14 samples · 4 unverified · 2 pointer-only (licence)
  Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
• 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.
• Fast Graph Representation Learning with PyTorch Geometric
  6 Mar 2019 6 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
• graph2vec: Learning Distributed Representations of Graphs
  17 Jul 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
• Structural Deep Network Embedding
  1 Jun 2016 6 repositories listed
  Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.
• 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.
• Contrastive Multi-View Representation Learning on Graphs
  10 Jun 2020 5 repositories listed Syntology ran 16 of 22 samples · 6 unverified · 9 pointer-only (licence)
  We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol.
• 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.
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
  31 Jul 2019 5 repositories listed
  There are also some recent methods based on language models (e.
• SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
  24 Nov 2017 5 repositories listed
  We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.
• 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…
• Energy Transformer
  14 Feb 2023 4 repositories listed Syntology ran 8 of 13 samples · 5 unverified · 3 pointer-only (licence)
  Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
• 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.
• 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…
• 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…
• 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}.
• 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.
• Composition-based Multi-Relational Graph Convolutional Networks
  8 Nov 2019 4 repositories listed Syntology ran 2 of 6 samples · 4 unverified · 3 pointer-only (licence)
  Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.

Syntology lines on 22 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.