papers_100M Example

This example shows how to use the remote backend feature of Kùzu to work with a large graph of papers and citations on a single machine. The data used in this example is ogbn-papers100M from the Open Graph Benchmark. The dataset contains approximately 100 million nodes and 1.6 billion edges.

Prepare the data

1. Download the dataset from http://snap.stanford.edu/ogb/data/nodeproppred/papers100M-bin.zip and put the *.zip file into this directory.
2. Run python prepare_data.py. The script will automatically extract the data and convert it to the format that Kùzu can read. A Kùzu database instance is then created under papers_100M and the data is loaded into the it.

Train a Model

Afterwards, run python train.py to train a three-layer GraphSAGE model on this dataset.