Node Property Prediction

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 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

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 54 papers with code (54 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.

• SSD: Single Shot MultiBox Detector
  8 Dec 2015 221 repositories listed Syntology ran 19 of 131 samples · 112 unverified · 5 pointer-only (licence)
  Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified…
• 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.
• Open Graph Benchmark: Datasets for Machine Learning on Graphs
  2 May 2020 21 repositories listed Syntology ran 2 of 17 samples · 15 unverified · 15 pointer-only (licence)
  We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
• 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 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.
• 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.
• 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.
• Temporal Graph Benchmark for Machine Learning on Temporal Graphs
  3 Jul 2023 6 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
• 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.
• 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.
• Representation Learning on Graphs with Jumping Knowledge Networks
  9 Jun 2018 5 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
• Long-range Meta-path Search on Large-scale Heterogeneous Graphs
  17 Jul 2023 4 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational…
• SCR: Training Graph Neural Networks with Consistency Regularization
  8 Dec 2021 4 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)
  However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
  29 Oct 2021 4 repositories listed Syntology ran 6 of 20 samples · 14 unverified
  We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
• Training Graph Neural Networks with 1000 Layers
  14 Jun 2021 4 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
• Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
  18 May 2021 4 repositories listed
  Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks.
• 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}.
• Heterogeneous Graph Transformer
  3 Mar 2020 4 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
  31 May 2023 3 repositories listed Syntology ran 4 of 7 samples · 3 unverified · 1 pointer-only (licence)
  With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual…
• Robust Optimization as Data Augmentation for Large-scale Graphs
  19 Oct 2020 3 repositories listed Syntology ran 3 of 15 samples · 12 unverified
  Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
• Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
  8 Sep 2020 3 repositories listed Syntology ran 3 of 7 samples · 4 unverified
  Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.
• Towards Deeper Graph Neural Networks
  18 Jul 2020 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
• DeeperGCN: All You Need to Train Deeper GCNs
  13 Jun 2020 3 repositories listed
  Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
• Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
  31 May 2023 2 repositories listed
  Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness.
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
  14 Oct 2022 2 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
• Simple and Efficient Heterogeneous Graph Neural Network
  6 Jul 2022 2 repositories listed
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
• Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
  16 Jun 2021 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.
• Bag of Tricks for Node Classification with Graph Neural Networks
  24 Mar 2021 2 repositories listed
  Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.

Syntology lines on 23 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.