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[R] Scalable graph machine learning: a mountain we can climb?

Graph machine learning is still a relatively new and developing area of research and brings with it a bucket load of complexities and challenges. One such challenge that both fascinates and infuriates those of us working with graph algorithms is — scalability.

I learned first-hand that when trying to apply graph machine learning techniques to identify fraudulent behaviour in the bitcoin blockchain data, scalability was the biggest roadblock. The bitcoin blockchain graph I used has millions of wallets (nodes) and billions of transactions (edges) which makes most graph machine learning methods infeasible.

An algorithm called GraphSAGE (based on the method of neighbour-sampling) offered some solid breakthroughs, but there are still mountains to climb to make scalable graph machine learning more practical.

https://medium.com/stellargraph/scalable-graph-machine-learning-a-mountain-we-can-climb-753dccc572f

submitted by /u/StellarGraphLibrary
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.