Network Representation Learning: A Survey

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Research output: Journal PublicationReview articlepeer-review

357 Citations (Scopus)

Abstract

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.

Original languageEnglish
Article number8395024
Pages (from-to)3-28
Number of pages26
JournalIEEE Transactions on Big Data
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Mar 2020
Externally publishedYes

Keywords

  • graph mining
  • Information networks
  • network embedding
  • network representation learning

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

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