Graph Sampling

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 44 papers with code (101 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.

• 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.
• 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.
• 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.
• 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.
• Accurate, Efficient and Scalable Training of Graph Neural Networks
  5 Oct 2020 2 repositories listed
  For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning.
• Accurate, Efficient and Scalable Graph Embedding
  28 Oct 2018 2 repositories listed
  However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques are unable to achieve scalability without losing…
• Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
  17 Apr 2025 1 repository listed
  This paper provides a comprehensive characterization of PP-GNNs, comparing them with graph-sampling-based methods in training efficiency, scalability, and accuracy.
• Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction
  2 Mar 2025 1 repository listed
  Graph sampling based Graph Convolutional Networks (GCNs) decouple the sampling from the forward and backward propagation during minibatch training, which exhibit good scalability in terms of layer depth and graph size.
• Retrofitting Temporal Graph Neural Networks with Transformer
  9 Sep 2024 1 repository listed
  We also adapt Transformer codebase to train TF-TGN efficiently with multiple GPUs.
• AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
  24 May 2024 1 repository listed
  We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and…
• A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
  20 Mar 2024 1 repository listed
  Experiments on real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling in terms of accuracy and time…
• Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
  18 Mar 2024 1 repository listed
  Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1, 000 scientific papers into an ontological knowledge graph.
• Cooperative Minibatching in Graph Neural Networks
  19 Oct 2023 1 repository listed Syntology ran 1 of 9 samples · 8 unverified
  Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive.
• Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
  9 Oct 2023 1 repository listed
  In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag sampler'', that targets a probability…
• GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
  5 Oct 2023 1 repository listed
  To this end, we introduce GRAPES, an adaptive sampling method that learns to identify the set of nodes crucial for training a GNN.
• A Topology-aware Analysis of Graph Collaborative Filtering
  21 Aug 2023 1 repository listed
  The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF).
• Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
  8 Aug 2023 1 repository listed Syntology ran 6 of 10 samples · 4 unverified · 10 pointer-only (licence)
  Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models.
• Graph Sampling-based Meta-Learning for Molecular Property Prediction
  29 Jun 2023 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction.
• Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
  28 Jun 2023 1 repository listed
  To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU…
• Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
  18 Jun 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  By dividing giant graph data, we build multiple independently and parallelly trained weaker GNNs (soup ingredient) without any intermediate communication, and combine their strength using a greedy interpolation soup…
• Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
  18 May 2023 1 repository listed
  Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver…
• Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
  7 Jan 2023 1 repository listed
  It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part.
• Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
  1 Jan 2023 1 repository listed
  To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation.
• Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
  6 Nov 2022 1 repository listed
  In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on…
• Joint Network Topology Inference via a Shared Graphon Model
  17 Sep 2022 1 repository listed
  The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information.
• Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
  13 Jul 2022 1 repository listed
  In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph.
• TF-GNN: Graph Neural Networks in TensorFlow
  7 Jul 2022 1 repository listed
  TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
• HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
  6 Dec 2021 1 repository listed
  Graph-based architectures are becoming increasingly popular as a tool for structure generation.
• Learning Dynamic Preference Structure Embedding From Temporal Networks
  23 Nov 2021 1 repository listed
  Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
• Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
  11 Aug 2021 1 repository listed
  Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs.

Syntology lines on 7 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.