research-article

Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text Classification

Authors:

Mingrui LiuAuthors Info & Claims

ACM Transactions on the Web, Volume 17, Issue 3

Article No.: 21, Pages 1 - 27

https://doi.org/10.1145/3580508

Published: 22 May 2023 Publication History

Abstract

The prevalent heterogeneous Graph Neural Network (GNN) models learn node and graph representations using pre-defined meta-paths or only automatically discovering meta-paths. However, the existing methods suffer from information loss due to neglecting undiscovered meta-structures with richer semantics than meta-paths in heterogeneous graphs. To take advantage of the current rich meta-structures in heterogeneous graphs, we propose a novel approach called HeGTM to automatically extract essential meta-structures (i.e., meta-paths and meta-graphs) from heterogeneous graphs. The discovered meta-structures can capture more prosperous relations between different types of nodes that can help the model to learn representations. Furthermore, we apply the proposed approach for text classification. Specifically, we first design a heterogeneous graph for the text corpus, and then apply HeGTM on the constructed text graph to learn better text representations that contain various semantic relations. In addition, our approach can also be used as a strong meta-structure extractor for other GNN models. In other words, the auto-discovered meta-structures can replace the pre-defined meta-paths. The experimental results on text classification demonstrate the effectiveness of our approach to automatically extracting informative meta-structures from heterogeneous graphs and its usefulness in acting as a meta-structure extractor for boosting other GNN models.

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Index Terms

Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 17, Issue 3

August 2023

302 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3597636

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 22 May 2023

Online AM: 30 January 2023

Accepted: 20 October 2022

Revised: 31 August 2022

Received: 18 February 2022

Published in TWEB Volume 17, Issue 3

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Natural Science Foundation of Hebei Province
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Cited By

Su CJiang QHan YWang THe Q(2025)Knowledge graph-driven decision support for manufacturing process: A graph neural network-based knowledge reasoning approachAdvanced Engineering Informatics10.1016/j.aei.2024.10309864(103098)Online publication date: Mar-2025
https://doi.org/10.1016/j.aei.2024.103098
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