https://github.com/oracle-samples/pgx-samples
Applications using Parallel Graph AnalytiX (PGX) from Oracle Labs
https://github.com/oracle-samples/pgx-samples
graph graph-algorithms graph-analytics graph-machine-learning machine-learning
Last synced: 11 months ago
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Applications using Parallel Graph AnalytiX (PGX) from Oracle Labs
- Host: GitHub
- URL: https://github.com/oracle-samples/pgx-samples
- Owner: oracle-samples
- License: other
- Created: 2019-03-18T20:49:00.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-01-29T19:56:26.000Z (about 1 year ago)
- Last Synced: 2025-03-31T11:06:37.921Z (11 months ago)
- Topics: graph, graph-algorithms, graph-analytics, graph-machine-learning, machine-learning
- Language: Jupyter Notebook
- Homepage: https://www.oracle.com/technetwork/oracle-labs/parallel-graph-analytix/overview/index.html
- Size: 3.66 MB
- Stars: 49
- Watchers: 9
- Forks: 28
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Security: SECURITY.md
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README
# PGX Samples
PGX (Parallel Graph AnalytiX) is a graph toolkit that provides a graph query language, optimized analytics algorithms, and machine learning support to extract insights hidden in the connections across datasets at high-performance and extreme scale.
While graphs have become ubiquitous nowadays as the backbone of multiple applications - from search engines and recommender systems to intelligent chatbots, PGX enables fast graph analysis on such industry-scale graphs (with trillions of edges) to reveal latent information between linked entities.
## Table of contents:
- [Built-in Algorithms](./built-in-algorithms/README.md)
## Contributing
This project welcomes contributions from the community. Before submitting a pull request, please [review our contribution guide](./CONTRIBUTING.md)
## Security
Please consult the [security guide](./SECURITY.md) for our responsible security vulnerability disclosure process
## License
Copyright (c) 2019, 2024 Oracle and/or its affiliates.
Released under the Universal Permissive License v1.0 as shown at
.