Graph 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

10 datasets 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 686 papers with code (1,570 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 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.
• 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.
• 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.
• 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.
• 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.
• Topological Deep Learning: Going Beyond Graph Data
  1 Jun 2022 4 repositories listed
  Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs,…
• 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.
• Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
  7 Feb 2024 3 repositories listed Syntology ran 10 of 13 samples · 3 unverified
  We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
• CaT: Balanced Continual Graph Learning with Graph Condensation
  18 Sep 2023 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Recent replay-based methods intend to solve this problem by updating the model using both (1) the entire new-coming data and (2) a sampling-based memory bank that stores replayed graphs to approximate the distribution…
• STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual Diarization
  18 Jun 2023 3 repositories listed
  This report introduces our novel method named STHG for the Audio-Visual Diarization task of the Ego4D Challenge 2023.
• Link Prediction without Graph Neural Networks
  23 May 2023 3 repositories listed
  Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications.
• Simplifying Subgraph Representation Learning for Scalable Link Prediction
  29 Jan 2023 3 repositories listed
  Link prediction on graphs is a fundamental problem.
• Graph Matching with Bi-level Noisy Correspondence
  8 Dec 2022 3 repositories listed Syntology ran 7 of 7 samples · 0 unverified · 7 pointer-only (licence)
  In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence…
• Enhanced graph-learning schemes driven by similar distributions of motifs
  11 Jul 2022 3 repositories listed
  Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity…
• GraphMAE: Self-Supervised Masked Graph Autoencoders
  22 May 2022 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs,…
• Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  11 Feb 2022 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.
• Global Self-Attention as a Replacement for Graph Convolution
  7 Aug 2021 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on…
• Bilinear Scoring Function Search for Knowledge Graph Learning
  1 Jul 2021 3 repositories listed
  We first set up a search space for AutoBLM by analyzing existing scoring functions.
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
  17 May 2021 3 repositories listed
  Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a…
• Learning on Attribute-Missing Graphs
  3 Nov 2020 3 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 6 pointer-only (licence)
  Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.
• Self-supervised Graph Learning for Recommendation
  21 Oct 2020 3 repositories listed
  In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
• Lifelong Graph Learning
  1 Sep 2020 3 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional…
• Generative 3D Part Assembly via Dynamic Graph Learning
  14 Jun 2020 3 repositories listed
  Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and…
• 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.
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
  24 May 2020 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
• Diffusion Improves Graph Learning
  28 Oct 2019 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).
• Neighborhood and Graph Constructions using Non-Negative Kernel Regression
  21 Oct 2019 3 repositories listed
  Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications.
• Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
  24 Jun 2025 2 repositories listed
  Equivariant Graph Neural Networks (GNNs) have achieved remarkable success across diverse scientific applications.
• HSG-12M: A Large-Scale Spatial Multigraph Dataset
  10 Jun 2025 2 repositories listed Syntology ran 6 of 14 samples · 8 unverified
  Existing graph benchmarks assume non-spatial, simple edges, collapsing physically distinct paths into a single link.

Syntology lines on 16 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.