Sampling from Large Heterogeneous Graphs with TF-GNN

In this new blogpost, Brandon Mayer and Bryan Perozzi go into details on how to organize scalable neighborhood sampling over large heterogeneous graphs (of many node types and edge types) using the example of OGB MAG dataset (2M nodes, 20M edges). Sampling can be defined using Apache Beam configs and can fetch data right from the Google Cloud Platform through the Dataflow Engine.

Recently, we covered the release of TensorFlow-GNN (TF-GNN), a new framework by Google to train GNNs on very large graphs that often do not fit into main memory. Today’s post is a more hands-on tutorial with particular code examples you could try yourself 🛠️.