Simply described, graph machine learning (Graph ML) is a subset of machine learning that deals with graph data. Graphs are made up of nodes that may or may not have feature vectors attached to them, and edges that may or may not have feature vectors attached to them.
Yes, graph theory is really beneficial when working on new learning and inference approaches for probabilistic graphic models. There has been a lot of work done on using graph-cuts to accomplish exact and approximate inference on graphical models with applications in computer vision.
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