Node Classification

Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.

Benchmarks archive 2025-07-28

138 leaderboard tables shown for this task, 138 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 138 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

75 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 75 until expanded.

Subtasks archive 2025-07-28

5 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 991 papers with code (1,860 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.
• Revisiting Semi-Supervised Learning with Graph Embeddings
  29 Mar 2016 26 repositories listed Syntology ran 15 of 28 samples · 13 unverified · 4 pointer-only (licence)
  We present a semi-supervised learning framework based on graph embeddings.
• 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.
• node2vec: Scalable Feature Learning for Networks
  3 Jul 2016 20 repositories listed Syntology ran 8 of 25 samples · 17 unverified · 3 pointer-only (licence)
  Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
• 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.
• 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…
• DeepWalk: Online Learning of Social Representations
  26 Mar 2014 14 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 1 pointer-only (licence)
  We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
• 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.
• Deep Graph Infomax
  27 Sep 2018 11 repositories listed Syntology ran 24 of 35 samples · 11 unverified · 9 pointer-only (licence)
  We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
  26 Feb 2019 10 repositories listed Syntology ran 3 of 11 samples · 8 unverified · 1 pointer-only (licence)
  Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
• Graph Random Neural Network for Semi-Supervised Learning on Graphs
  22 May 2020 9 repositories listed Syntology ran 8 of 22 samples · 14 unverified
  We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
• LINE: Large-scale Information Network Embedding
  12 Mar 2015 9 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
• 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.
• GraphSAINT: Graph Sampling Based Inductive Learning Method
  10 Jul 2019 8 repositories listed
  Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
• 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.
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
  27 Oct 2020 7 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 2 pointer-only (licence)
  Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
  3 Sep 2019 7 repositories listed Syntology ran 3 of 39 samples · 36 unverified · 4 pointer-only (licence)
  Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
  25 Jul 2019 7 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  \emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.
• 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.
• 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.
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
  20 May 2019 6 repositories listed
  Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.
• 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.
• Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
  27 Oct 2021 5 repositories listed Syntology ran 7 of 21 samples · 14 unverified · 4 pointer-only (licence)
  Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other.
• SIGN: Scalable Inception Graph Neural Networks
  23 Apr 2020 5 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.
• Inductive Representation Learning on Temporal Graphs
  19 Feb 2020 5 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Syntology lines on 27 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.