Efficient Machine Learning On Large-Scale Graphs

Machine learning on graph data has become a common area of interest across academia and industry. However, due to the size of real-world industry graphs (hundreds of millions of vertices and billions of edges) and the special architecture of graph neural networks, it is still a challenge for practitioners and researchers to perform machine learning tasks on large-scale graph data. It typically takes a powerful and expensive GPU machine to train a graph neural network on a million-vertex scale graph, let alone doing deep learning on real enterprise graphs. In this tutorial, we will cover how to develop and run performant graph algorithms and graph neural network models with TigerGraph [3], a massively parallel platform for graph analytics, and its Machine Learning Workbench with PyTorch Geometric [4] and DGL [8] support. Using an NFT transaction dataset [6], we will first investigate transactions using graph algorithms by themselves as methods of graph traversing, clustering, classification, and determining similarities between data. Secondly, we will show how to use those graph-derived features such as PageRank and embeddings to empower traditional machine learning models. Finally, we will demonstrate how to train common graph neural networks with TigerGraph and how to implement novel graph neural network models. Participants will use the TigerGraph ML Workbench Cloud to perform graph feature engineering and train their machine learning algorithms during the session.