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Related Experiment Video

Updated: Jun 6, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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SkyMap: a generative graph model for GNN benchmarking.

Axel Wassington1, Raúl Higueras1, Sergi Abadal1

  • 1Department of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, Spain.

Frontiers in Artificial Intelligence
|November 29, 2024
PubMed
Summary
This summary is machine-generated.

SkyMap generates synthetic labeled attributed graphs, improving Graph Neural Network (GNN) performance replication. This new model offers fine-grained control over graph topology and features, outperforming existing generative methods for GNN benchmarking.

Keywords:
Graph Neural Network (GNN)benchmarkdegree distributiongraph generation modelmachine learning datasetsmixing matrix

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Area of Science:

  • Machine Learning
  • Graph Theory
  • Data Science

Background:

  • Graph Neural Networks (GNNs) are increasingly popular, but research is limited by a small set of benchmark datasets.
  • Existing generative models for synthetic graphs (e.g., ALBTER, GenCAT) often fail to accurately reflect GNN performance on original data.

Purpose of the Study:

  • To introduce SkyMap, a novel generative model for labeled attributed graphs.
  • To provide fine-grained control over graph topology and feature distributions for synthetic data generation.
  • To enhance the evaluation and benchmarking of GNNs using more diverse and representative synthetic datasets.

Main Methods:

  • Developed SkyMap, a generative model for labeled attributed graphs with controllable topology and feature distributions.
  • Evaluated SkyMap's ability to replicate graph learnability across various GNN architectures (graph convolutional, attention, isomorphism networks).
  • Quantified performance replication using Wasserstein distance and demonstrated dataset constellation generation via parameter sampling.

Main Results:

  • SkyMap demonstrated superior performance in replicating GNN learnability compared to ALBTER and GenCAT, achieving a 64% lower Wasserstein distance.
  • The model enables the creation of diverse synthetic graph datasets by sampling input parameters.
  • A performance comparison between GNNs and multilayer perceptrons illustrated the utility of SkyMap-generated datasets.

Conclusions:

  • SkyMap offers a significant advancement in generating high-fidelity synthetic graph datasets for GNN evaluation.
  • The model's fine-grained control and ability to generate diverse datasets address the limitations of current GNN benchmarking practices.
  • SkyMap facilitates more robust and reliable GNN research by providing tailored synthetic data.