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Motif-guided Heterogeneous Graph Deep Generation.

Chen Ling1, Carl Yang1, Liang Zhao1

  • 1Department of Computer Science, Emory University, Atlanta, 30332, GA, USA.

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Summary
This summary is machine-generated.

This study introduces HGEN, a novel framework for generating high-quality heterogeneous graphs. HGEN preserves both local semantics and global distributions, advancing heterogeneous graph representation learning.

Keywords:
Deep Generative ModelsGraph GenerationGraph Neural NetworkHeterogeneous Graph

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Area of Science:

  • Computer Science
  • Data Science
  • Graph Theory

Background:

  • Real-world complex systems involve diverse objects and relations, often represented by heterogeneous graphs.
  • Heterogeneous graphs capture multi-modal interactions but their generation is challenging.
  • Existing methods fail to preserve both local semantics and higher-order structural information.

Purpose of the Study:

  • To develop an end-to-end framework for generating novel, high-quality heterogeneous graphs.
  • To address limitations in existing methods regarding semantic and structural information preservation.
  • To provide robust benchmarks for heterogeneous representation learning tasks.

Main Methods:

  • Introduced HGEN, a framework incorporating a heterogeneous walk generator.
  • Developed a network motif generator to capture higher-order structural distributions.
  • Utilized a heterogeneous graph assembler for adaptive graph construction.

Main Results:

  • The proposed method theoretically guarantees preservation of local semantics and global heterogeneous distribution.
  • Comprehensive experiments demonstrate the effectiveness and efficiency of HGEN.
  • Generated graphs serve as valuable benchmarks for downstream tasks.

Conclusions:

  • HGEN offers a powerful and efficient solution for generating realistic heterogeneous graphs.
  • The framework successfully preserves crucial graph properties often lost in existing methods.
  • This work advances the field of heterogeneous graph generation and representation learning.