Survey on graph embeddings and their applications to machine learning problems on graphs.

ABSTRACT: Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their descr

SUBMITTER: Makarov I 

PROVIDER: S-EPMC7959646 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature