A Survey on Network Embedding

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 evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.

References in corpus (3)

• Hyperbolic Geometry of Complex Networks
• Heterogeneous Information Network Embedding for Meta Path based Proximity
• Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm

Cited by in corpus (6)

• Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
• Deep Collaborative Embedding for information cascade prediction
• EPNE: Evolutionary Pattern Preserving Network Embedding
• LNEMLC: Label Network Embeddings for Multi-Label Classification
• Multimodal Deep Network Embedding with Integrated Structure and Attribute Information
• Piecewise linear regressions for approximating distance metrics