Network Representation Learning: A Survey

TL;DR

This survey comprehensively reviews network representation learning, categorizing techniques, evaluation methods, and empirical performance, highlighting recent advances and future research directions in embedding complex networks into low-dimensional spaces.

Contribution

It introduces new taxonomies for categorizing network representation learning methods and provides empirical comparisons and analysis of their performance and complexity.

Findings

Network embedding techniques effectively preserve network topology.

Evaluation protocols and benchmark datasets are standardized.

Empirical results highlight the strengths and limitations of different algorithms.

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.