TigerGraph ML Workbench

TigerGraph Machine Learning Workbench is a platform designed for data scientists and AI/ML practitioners to easily develop graph-based machine learning models with production-scale graph data stored in TigerGraph.

It is available as a service through TigerGraph Cloud or standalone in two editions, Community and Enterprise. It provides robust and efficient data pipelines at the Python level to interact with the TigerGraph database to perform common data processing functions such as:

  • data partitioning

  • subgraph sampling

  • graph feature generation

  • data batching and/or streaming for ML model development, training, and inference purposes.

ML Workbench on Cloud provides an ML solution from development to deployment including a fully-managed Jupyter notebook instance with:

  • on-demand computation

  • distributed AutoML for hyperparameter tuning

  • ML pipeline management & orchestration

  • model deployment and serverless production serving

ML Workbench also comes in standalone Community or Enterprise editions as a Jupyter-based Python development framework for direct integration with existing machine learning infrastructure.

It is compatible with other popular ML frameworks such as PyTorch Geometric, DGL, and TensorFlow as well as cloud infrastructure including Amazon SageMaker, Google Vertex AI and Microsoft Azure.

Graph neural networks (GNNs) and their applications

GNNs tend to outperform other machine learning techniques when there are well-defined relationships between data as it directly models the connectivity of your graph data. In recent research, GNNs have proven their success across various business domains and applications. With TigerGraph ML Workbench, you can now easily explore the potentials of GNN for your domains.

Here are some papers and resources to spark ideas in a range of applications and industries:

Recommendation Engines

Pinterest introduced PinSAGE[1], an architecture that can serve real-time recommendations to their users, resulting in a 10-30% improvement compared to other deep learning methods when evaluated in A/B testing.

Supply Chain

Amazon released a GNN architecture[2] that incorporates temporal information with GNNs for demand forecasting. The method models interactions between products and their sellers on Amazon in a graph, resulting in a 16% improvement over other state-of-the-art forecasting methods.

Healthcare

AstraZeneca has used graph neural networks like GraphSAGE to generate knowledge graph embeddings for predicting possible drug-drug interactions such as possible synergies between drugs, as well as possible polypharmacy side effects[3]. Additionally, the possibility of repurposing drugs to treat COVID has been studied using a drug repurposing knowledge graph and GNNs[4].

Financial Institutions

GCNs have been studied for predicting money-laundering behavior in Bitcoin transaction networks, and have been shown to perform admirably compared to other approaches[5].

If you are interested in learning more about the fundamental research on different variations of GNNs, here is a list of helpful publications:

1. Ying, Rex et al. “Graph Convolutional Neural Networks for Web-Scale Recommender Systems”, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018.
2. Ankit Gandhi, Aakankasha, Sivaramakrishnan Kaveri, Vineet Chaoji, “Spatio-temporal multi-graph networks for demand forecasting in online marketplaces”
3. Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, Eliseo Papa, “A Unified View of Relational Deep Learning for Drug Pair Scoring”, CoRR, November 2021.
4. Hsieh, K., Wang, Y., Chen, L. et al. “Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence”, Sci Rep 11, 23179, 2021.
5. Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, and Charles E. Leiserson, “Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics”, In Proceedings of ACM Conference (KDD ’19 Workshop on Anomaly Detection in Finance), 2019.