Graph Representation Learning

Description from the archive archive 2025-07-28.

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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 479 papers with code (982 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.

• 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.
• 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…
• 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.
• 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.
• QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
  13 Apr 2021 6 repositories listed Syntology ran 1 of 25 samples · 24 unverified · 4 pointer-only (licence)
  The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify…
• 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.
• Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks
  13 Aug 2023 5 repositories listed
  These findings underscore the efficacy of the proposed loss functions in dynamic network modeling.
• 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.
• 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.
• 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.
• GraphGAN: Graph Representation Learning with Generative Adversarial Nets
  22 Nov 2017 5 repositories listed
  The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
• 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.
• Large-Scale Representation Learning on Graphs via Bootstrapping
  12 Feb 2021 4 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 7 pointer-only (licence)
  To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.
• 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.
• Understanding Negative Sampling in Graph Representation Learning
  20 May 2020 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.
• Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
  18 Aug 2023 3 repositories listed Syntology ran 5 of 26 samples · 21 unverified
  In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations.
• 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…
• Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
  17 May 2023 3 repositories listed
  We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries.
• A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
  22 Feb 2023 3 repositories listed
  Graphs without this property are called heterophilous, and it is typically assumed that specialized methods are required to achieve strong performance on such graphs.
• Simplifying Subgraph Representation Learning for Scalable Link Prediction
  29 Jan 2023 3 repositories listed
  Link prediction on graphs is a fundamental problem.
• 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.
• Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
  28 Feb 2022 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many…
• A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
  16 Feb 2022 3 repositories listed
  Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).
• Structure-Aware Transformer for Graph Representation Learning
  7 Feb 2022 3 repositories listed Syntology ran 4 of 9 samples · 5 unverified
  Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.
• E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
  30 Mar 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
• Towards a Unified Framework for Fair and Stable Graph Representation Learning
  25 Feb 2021 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn…
• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
  22 Sep 2020 3 repositories listed
  Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the…
• 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.
• Deep Graph Contrastive Representation Learning
  7 Jun 2020 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
• A Gentle Introduction to Deep Learning for Graphs
  29 Dec 2019 3 repositories listed
  The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.

Syntology lines on 19 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.