The taxonomy of graph embedding machine learning algorithms
A wide variety of methods to generate a compact space for graph representation have been developed. In recent years, a trend has been observed of researchers and machine learning practitioners converging toward a unified notation to provide a common definition to describe such algorithms. In this section, we will be introduced to a simplified version of the taxonomy defined in the paper Machine Learning on Graphs: A Model and Comprehensive Taxonomy (https://arxiv.org/abs/2005.03675).
In this formal representation, every graph, node, or edge embedding method can be described by two fundamental components, named the encoder and the decoder. The encoder (ENC) maps the input into the embedding space, while the decoder (DEC) decodes structural information about the graph from the learned embedding (Figure 2.7).
The framework described in the paper follows an intuitive idea: if we are able to encode a graph such that the...
