Abstract
The primary challenge of applying machine learning in graph theory is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. In this chapter, we will look at a review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph convolutional networks. We will also look at methods to embed individual nodes as well as approaches to embed entire (sub)graphs.
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Raj P. M., K., Mohan, A., Srinivasa, K.G. (2018). Representation Learning on Graphs. In: Practical Social Network Analysis with Python. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-96746-2_15
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