Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification

Küçük Resim Yok

Tarih

2024

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Wien

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Graph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like classification, clustering, and link prediction. As a result, it has become a key method in various research areas. However, different embedding methods may be used depending on the variety of graphs available. One of the most commonly used graph types is the heterogeneous graph (HG) or heterogeneous information network (HIN), which presents unique challenges for graph embedding approaches due to its diverse set of nodes and edges. Several methods have been proposed for heterogeneous graph embedding in recent years to overcome these challenges. This paper aims to review the latest techniques used for this purpose, divided into two main parts: the first part describes the fundamental concepts and obstacles in heterogeneous graph embedding, while the second part compares the most critical methods. Finally, the results are summarized, outlining the challenges and opportunities for future directions.

Açıklama

Anahtar Kelimeler

Heterogeneous Graph Embedding, Deep Learning, Node Classification, Heterogeneous Network, Heterogeneous Graph Transformer

Kaynak

Social Network Analysis and Mining

WoS Q Değeri

N/A

Scopus Q Değeri

Q1

Cilt

14

Sayı

1

Künye