Implement GraphWorld for fake graphs benchmarking

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Ref.
GraphWorld: Fake Graphs Bring Real Insights for GNNs,
GraphWorld: Advances in Graph Benchmarking
https://github.com/google-research/graphworld

A user of GraphWorld decides on a generative model for the task (in this case, node classification). GraphWorld comes with default generative models for node classification, link prediction, and graph property prediction

We propose GraphWorld as a complementary GNN benchmark that allows researchers to explore GNN performance on regions of graph space that are not covered by popular academic datasets. Furthermore, GraphWorld is cost-effective, running hundreds-of-thousands of GNN experiments on synthetic data with less computational cost than one experiment on a large OGB dataset.