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https://github.com/shivam-880/examples

Last synced: 7 days ago
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Host: GitHub
URL: https://github.com/shivam-880/examples
Owner: shivam-880
License: apache-2.0
Created: 2021-11-21T09:16:08.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2021-11-28T18:13:50.000Z (almost 3 years ago)
Last Synced: 2024-10-09T10:01:56.025Z (28 days ago)
Language: Scala
Size: 4.27 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        


  



Raphtory Examples


[![test and build](https://github.com/Raphtory/Raphtory/actions/workflows/test_and_build.yml/badge.svg?branch=master&event=push)](https://github.com/Raphtory/Raphtory/actions/workflows/test_and_build.yml?query=branch%3Amaster+event%3Apush++)

[![test and build](https://github.com/Raphtory/Raphtory/actions/workflows/test_and_build.yml/badge.svg?event=schedule)](https://github.com/Raphtory/Raphtory/actions/workflows/test_and_build.yml?query=event%3Aschedule++)

[![Latest Tag](https://img.shields.io/github/v/tag/Raphtory/Raphtory?include_prereleases&sort=semver&color=brightgreen)](https://github.com/Raphtory/Raphtory/tags)

[![Latest Release](https://img.shields.io/github/v/release/Raphtory/Raphtory?color=brightgreen&include_prereleases&sort=semver)](https://github.com/Raphtory/Raphtory/releases)

Raphtory is an open-source platform for distributed real-time temporal graph analytics, allowing you to load and process large dynamic datsets across time. If you would like a brief summary of what its used for before fully diving into the getting start guide please check out this [article](https://www.turing.ac.uk/blog/just-add-time-dizzying-potential-dynamic-graphs) from the Alan Turing Institute first!

## Table of Contents

- [Getting started](#getting-started)

- [Community and Changelog](#community-and-changelog)

- [Contributors](#contributors)

- [License](#license)

## Getting Started

The best way to get started with Raphtory is to vist our [website](https://raphtory.github.io/) where we have tutorials on how to use Raphtory for graph building, analysis, and more. Good entry points for this are:

- [Installation](https://raphtory.github.io/documentation/install)

- [Building a graph from your data](https://raphtory.github.io/documentation/sprouter)

- [Writing algorithms in Raphtory](https://raphtory.github.io/documentation/analysis-explained)

- [Six Degrees of Gandalf](https://raphtory.github.io/documentation/analysis-qs)

- [How to Deploy Raphtory](https://raphtory.github.io/documentation/deployment)

We also have a [page](https://raphtory.github.io/algorithms/) for algorithms implemented in Raphtory (both temporal and static). These can be used to analyse your own datasets once ingested or as a basis to implement your own custom algorithms.

### Too Long Didn't Read - Let me run it now!

If you want to see how Raphtory runs without reading a mountain of documentation you can quickly get set up with an example Raphtory project via these steps:

1. Clone the [example](https://github.com/Raphtory/Examples) repo and pick one of the examples inside that takes your fancy. 

2. Download the [latest stable release](https://github.com/Raphtory/Raphtory/releases) of the `raphtory.jar` and move it into the lib directory of your chosen example project. 

   * **Here be dragons:** You can also pick the nightly build, but there may be some quirks yet to be ironed out. If you find any please report the issue on [Jira](https://raphtory.atlassian.net/jira/software/projects/RAPH/issues/) or on [Slack](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA).

3. Install SBT by following their [guide](https://www.scala-sbt.org/1.x/docs/Setup.html). The example project uses SBT to compile the source code. 

4. Initiate SBT by changing into the example project directory in the terminal and running the command `sbt`. You will know when the SBT interactive shell has started once it shows `>`.

5. Execute `compile` to build the project. 

6. Execute `run` to start the project. You will then see Raphtory build the graph and execute an algorithm relevant to your chosen dataset.

7. The rest is then up to you - feel free to explore the data, submit different algorithms and ask any questsions you have on the Raphtory [Slack](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA)!   

**Note:** Raphtory is built with Scala. We recommend using IntelliJ IDE for your code. They have a community version which is free. Follow their [guide](https://www.jetbrains.com/idea/download/#section=windows) for installation.

## Community and Changelog  

- Follow the latest development on the official [blog](https://raphtory.github.io/blog/)

- Follow the Raphtory [Twitter](https://twitter.com/raphtory)

- Join the [Slack](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA) group (we are always happy to answer any questions and chat about the project!) Feel free to join the #raphtory-development and #askaway channel to discuss current issues or ask any questions.

- Raise issues and bugs on the Raphtory [Jira](https://raphtory.atlassian.net/jira/software/projects/RAPH/issues/) board 

## Contributors



Want to get involved? Please join the Raphtory [Slack](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA) group and speak with us on how you could pitch in!

## License  

Raphtory is licensed under the terms of the Apache License (check out our LICENSE file).