[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.