https://github.com/theden/stellar
Machine Learning for Graphs
https://github.com/theden/stellar
Last synced: about 1 year ago
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Machine Learning for Graphs
- Host: GitHub
- URL: https://github.com/theden/stellar
- Owner: TheDen
- License: apache-2.0
- Created: 2018-06-06T04:15:43.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-06-06T15:03:17.000Z (about 8 years ago)
- Last Synced: 2024-05-30T01:18:41.957Z (about 2 years ago)
- Language: Shell
- Size: 665 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Stellar Graph Analytics
Stellar is a graph analytics platform designed for running algorithms on large scale graph data. It supports data integration, entity resolution, machine learning on graphs and visualisation.
## Features
* Data Integration
* Node Embedding for Machine Learning (Node2Vec)
* Graph Convolutional Networks (GCN)
* Entity Resolution
* Graphs searchable through ElasticSearch
* Dockerized containers for orchestration
* Apache Nifi for coordination
* Web UI for Data Integration
* Python Client
## Overview
Stellar is designed to:
* Merge data into a graph
* Run search, machine learning, and entity resolution across the graph
* Visualise results
This repository is the prototype release for the Stellar platform. The current limitations are as follows:
* Single User
* Single Machine
* 50,000 nodes
* CSV data only
* Numeric values for machine learning
* No missing values for machine learning
* Fixed datasets for entity resolution
* Web UI focused only on integration
* For Data Visualisation you will need to install [Gephi](https://gephi.org)
## Modules
Stellar is a collection of Docker containers coordinated with Apache Nifi. The collection of docker containers are built from the following repositories:
* [Coordinator](https://github.com/data61/stellar-coordinator)
* [Data Integration](https://github.com/data61/stellar-ingest)
* [Search](https://github.com/data61/stellar-search)
* [Entity Resolution](https://github.com/data61/stellar-ERBaseline)
* [Machine Learning](https://github.com/data61/stellar-evaluation-plugins)
* [Web UI](https://github.com/data61/stellar-config)
* [Python Client](https://github.com/data61/stellar-py)
This GitHub repository uses Docker Compose to download and launch the Docker containers. The containers are downloaded from DockerHub in the [Data61 repository](https://hub.docker.com/r/data61).
### Install
Stellar runs as a server application, and the user connects to the server through the Web UI or the Python Client.
To connect remotely to a Stellar server instance follow these [instructions](./doc/remote.md).
If you would like to install and run the Stellar server manually, you will need to follow the specifications and instructions as specified [here](./doc/server.md).
## License
Copyright (c) 2017-2018 [CSIRO Data61](http://data61.csiro.au/)
Licensed under the Apache License, Version 2.0 (the "License"); you may not
use the files included in this repository except in compliance with the
License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.