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Table Of Contents
Creators of Intelligence
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AA: You touched on one of your areas of research interest being around graph attention and graph representation learning. Can you please explain it in your own words? What does it mean in practice? What are some of the applications and limitations, and where’s the research heading?
PV: As I mentioned briefly, graph representation learning is probably my biggest passion in machine learning right now, and I think it’s a very important emerging area. Regardless of what area of computer science you choose to specialize in, we’re now at a point where you’ll probably come into contact with graph representation learning in one way or another.
Essentially, it deals with processing data that lives on graphs. Graphs are interconnected structures of nodes and edges. They’re a way to naturally represent networked data.
Figure 6.1 – A graph structure with nodes and edges
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