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Representation Learning on Graphs: Methods and Applications

Representation Learning on Graphs: Methods and ApplicationsWilliam L. of Computer ScienceStanford UniversityStanford, CA, 94305 AbstractMachine Learning on graphs is an important and ubiquitous task with Applications ranging from drugdesign to friendship recommendation in social networks. The primary challenge in this domain is findinga way to represent, or encode, graph structure so that it can be easily exploited by machine learningmodels. Traditionally, machine Learning approaches relied on user-defined heuristics to extract featuresencoding structural information about a graph ( , degree statistics or kernel functions).

dimensional visualization of node embeddings generated from this graph using the DeepWalk method (Section 2.2.2) [46]. The distances between nodes in the embedding space reflect proximity in the original graph, and the node embeddings are spatially clustered according to the different color-coded communities. Reprinted with permission from [46 ...

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