articleIEEE Transactions on Big DataMay 24, 2022Closed access

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

XWXiao WangDBDeyu BoCSChuan ShiSFShaohua FanYYYanfang Ye

Beijing University of Posts and Telecommunications · University of Notre Dame · +3 more institutions

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Abstract

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e.g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years. In this survey, we perform a comprehensive review of the recent development on HG embedding methods and techniques. We first introduce the basic concepts of HG and discuss the unique challenges brought by the heterogeneity for HG embedding in comparison with homogeneous graph representation learning; and…

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Topics & keywords

Topics
Keywords
  • Computer science
  • Embedding
  • Categorization
  • Graph
  • Theoretical computer science
  • Cluster analysis
  • Benchmarking
  • Data science
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
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