mlsanityData to 7 Oct 2026

SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation

arXiv 2401.04133 ↗First posted 7 Jan 2024cs.LG cs.AI cs.SIcited by 0

Not on the map. Why?

Abstract (arXiv)

Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for robust baselines and expansive graph datasets is accentuated, particularly in the context of Heterogeneous Information Networks (HIN). Addressing this, we introduce SynHING, a novel framework for Synthetic Heterogeneous Information Network Generation aimed at enhancing graph learning and explanation. SynHING systematically identifies major motifs in a target HIN and employs a bottom-up generation process with intra-cluster and inter-cluster merge modules. This process, supplemented by post-pruning techniques, ensures the synthetic HIN closely mirrors the original graph's structural and statistical properties. Crucially, SynHING provides ground-truth motifs for evaluating GNN explainer models, setting a new standard for explainable, synthetic HIN generation and contributing to the advancement of interpretable machine learning in complex networks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 7 Jan 2024.

Builds on

26 of 38 references.

References · 26

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  3. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  4. 2019Heterogeneous Graph Attention Networkcited by 732
  5. 2019Modeling Tabular data using Conditional GANcited by 695
  6. 2020Benchmarking Graph Neural Networkscited by 672
  7. 2020Heterogeneous Graph Transformercited by 581
  8. 2019Graph Transformer Networkscited by 520
  9. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  10. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  11. 2017Community Detection and Stochastic Block Modelscited by 317
  12. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
14 more
  1. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  2. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  3. 2020Graph Clustering with Graph Neural Networkscited by 163
  4. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  5. 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
  6. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
  7. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  8. 2022GraphWorld: Fake Graphs Bring Real Insights for GNNscited by 56
  9. 2019Overlapping Community Detection with Graph Neural Networkscited by 45
  10. 2022OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networkscited by 33
  11. 2022A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metricscited by 26
  12. 2020Pathfinder Discovery Networks for Neural Message Passingcited by 16
  13. 2022Synthetic Graph Generation to Benchmark Graph Learningcited by 16
  14. 2023Towards Fine-Grained Explainability for Heterogeneous Graph Neural Networkcited by 5

Built on by

Not cited yet.