A Survey on Network Embedding
Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu

TL;DR
This survey comprehensively reviews current network embedding techniques, categorizing methods, discussing evaluation approaches, and outlining future research directions in the field of network analysis.
Contribution
It provides a systematic overview of existing network embedding methods, including structure-preserving, property-preserving, and advanced techniques, along with evaluation resources and future prospects.
Findings
Categorization of network embedding methods
Overview of evaluation approaches and resources
Discussion of future research directions
Abstract
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks
