I have always wondered what common usecases such systems address. How large are the graphs typically? Are graphs really that big that they dont fit in a single machine?
Some of the usecase to build such graph is to get node embeddeding for fraud prevention or link prediction etc.
Transaction graphs can be huge. Web graphs are huge. Also there are also many other huge graphs and use cases, like spot irregularities / intrusions from network traffic graphs in Alibaba Cloud. Some bioinformation algorithms also requires the ability to process big graphs.
Typically the graph may have billions of nodes and 10x billions of edges. Obviously the graph data cannot be fit into a single machine to run alogrithms like SSSP or pagerank. And a single machine usually doesn't have enough cores for the computation, e.g., an interactive query couldn't return within milliseconds. That why we need distributed graph processing system for such big graphs.