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

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 description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how diﬀerent types of networks impact the ability of models to incorporate structural and attributed data into a uniﬁed embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classiﬁcation, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classiﬁcation, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how diﬀerent networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classiﬁcation, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing ﬁeld of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs. ABSTRACT 10 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. 17 Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a uniﬁed embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classiﬁcation, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classiﬁcation, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classiﬁcation, link prediction, clustering and graph visualization. 28 29 As a result, our survey covers a new rapidly growing ﬁeld of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs. low-dimensional embedding by 149 optimizing over a broad class of expressive neural network functions.