On Whole-Graph Embedding Techniques

Networks provide suitable representative models in many applications, ranging from social to life sciences. Such representations are able to capture interactions and dependencies among variables or observations, thus providing simple and powerful modeling of phenomena. Whole-graph embedding involves the projection of graphs into a vector space, while retaining their structural properties. In recent years, several embedding techniques using graph kernels, matrix factorization, and deep learning architectures have been developed to learn low-dimensional graph representations. These embeddings can be used for feature extraction, graph clustering, or building classification models. In this chapter, we survey embedding techniques that jointly embed whole graphs for classification tasks. We compare them and evaluate their performance on undirected synthetic and real-world network datasets. The datasets and software adopted for our experiments are made publicly available for further comparisons.