A Tutorial on Network Embeddings

Abstract

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.

arXiv comment23 pages, 6 figures

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Cited by in corpus (13)

• Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
• Grale: Designing Networks for Graph Learning
• Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
• Improving Question Answering by Commonsense-Based Pre-Training
• MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit
• InstantEmbedding: Efficient Local Node Representations
• Adversarial Attack on Network Embeddings via Supervised Network Poisoning
• Initialization for Network Embedding: A Graph Partition Approach
• Persistent Homology of Graph Embeddings
• A Survey on Role-Oriented Network Embedding
• A Weighted Quiver Kernel using Functor Homology