pyraphtory


Namepyraphtory JSON
Version 0.2.2 PyPI version JSON
download
home_page
SummaryRaphtory - Temporal Graph Analytics Platform. This is the Python version of the library.
upload_time2023-03-24 14:36:57
maintainer
docs_urlNone
authorFabian Murariu, Lucas Jeub, Ben Steer, Haaroon Yousaf
requires_python>=3.9
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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2020 Queen Mary University London Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file 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.
keywords raphtory pyraphtory graph analytics temporal
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coveralls test coverage No coveralls.
            <br>
<p align="center">
  <img src="https://user-images.githubusercontent.com/6665739/130641943-fa7fcdb8-a0e7-4aa4-863f-3df61b5de775.png" alt="Raphtory" height="100"/>
</p>
<p align="center">
</p>


<p align="center">
<a href="https://github.com/Raphtory/Raphtory/actions/workflows/test.yml/badge.svg">
<img alt="Test and Build" src="https://github.com/Raphtory/Raphtory/actions/workflows/test.yml/badge.svg" />
</a>
<a href="https://github.com/Raphtory/Raphtory/releases">
<img alt="Latest Release" src="https://img.shields.io/github/v/release/Raphtory/Raphtory?color=brightgreen&include_prereleases" />
</a>
<a href="https://github.com/Raphtory/Raphtory/issues">
<img alt="Issues" src="https://img.shields.io/github/issues/Raphtory/Raphtory?color=brightgreen" />
</a>
<a href="https://mybinder.org/v2/gh/Raphtory/Raphtory/v0.1.6?labpath=examples%2Fbinder_python%2Findex.ipynb">
<img alt="Launch Notebook" src="https://mybinder.org/badge_logo.svg" />
</a>
</p>
<p align="center">
<a href="https://www.raphtory.com">🌍 Website </a>
&nbsp
<a href="https://docs.raphtory.com/">📒 Documentation</a>
&nbsp 
<a href="https://www.pometry.com"><img src="https://user-images.githubusercontent.com/6665739/202438989-2859f8b8-30fb-4402-820a-563049e1fdb3.png" height="20" align="center"/> Pometry</a> 
&nbsp
<a href="https://docs.raphtory.com/en/master/Introduction/ingestion.html">🧙🏻‍ Tutorial</a> 
&nbsp
<a href="https://github.com/Raphtory/Raphtory/issues">🐛 Report a Bug</a> 
&nbsp
<a href="https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA"><img src="https://user-images.githubusercontent.com/6665739/154071628-a55fb5f9-6994-4dcf-be03-401afc7d9ee0.png" height="20" align="center"/> Join Slack</a> 
</p>

<br>

Raphtory is a powerful analytics engine for large-scale graph analysis. It lets you run complex queries on your data, no matter where it's stored or what format it's in. But that's not all - Raphtory's real superpower is its ability to track and explore the history of a complex system, from "time traveling" through data to executing advanced analysis like taint tracking, temporal reachability, and mining temporal motifs.

**Raphtory is easy to use:** just run a single pip install command and embed it with your existing Python/Pandas pipeline for input and output.

**Raphtory is expressive:** It's designed to represent all types of graph queries and has a well-developed API for exploring your data across its history.

**Raphtory is lightning-fast and scales effortlessly**: Built on Apache Arrow's storage and vectorized compute, Raphtory can be run on a laptop or a distributed cluster for terabyte-scale graphs.

# Running a basic example

```python
# Import Raphtory
import PyRaphtory

# Create a new local or distributed context
ctx = PyRaphtory.local()
graph = ctx.new_graph()

# Add some data to your graph
graph.add_vertex(1, 1)
graph.add_vertex(2, 2)
graph.add_vertex(3, 3)
graph.add_edge(4, 1, 2)
graph.add_edge(4, 1, 3)

# Collect some simple vertex metrics
# Ran across a range of the data with incremental windowing
df = graph
      .range(1,4,1)
      .window(1)
      .step(lambda vertex: vertex.set_state("name", vertex.name()))
      .step(lambda vertex: vertex.set_state("out_degree", vertex.out_degree())) 
      .step(lambda vertex: vertex.set_state("in_degree", vertex.in_degree()))
      .select("name", "out_degree", "in_degree")
      .to_df()

# Preview DataFrame
df

|    |   timestamp |   window |   name |   out_degree |   in_degree |
|----|-------------|----------|--------|--------------|-------------|
|  0 |           1 |        1 |      1 |            0 |           0 |
|  1 |           2 |        1 |      2 |            0 |           0 |
|  2 |           3 |        1 |      3 |            0 |           0 |
|  3 |           4 |        1 |      1 |            2 |           0 |
|  4 |           4 |        1 |      2 |            0 |           1 |
|  5 |           4 |        1 |      3 |            0 |           1 |
```

# Installing Raphtory 

Raphtory is available for Python and Scala/Java, with support for Rust planned in version 0.3.0. We recommend using the PyRaphtory client for Python, which includes everything you need and can be run locally or in distributed mode.

You should have Python version 3.9 or higher. It's a good idea to use conda, virtualenv, or pyenv. 

```bash
pip install pyraphtory
``` 

# Examples and Notebooks

Check out Raphtory in action with our interactive Jupyter Notebook! Just click the badge below to launch a Raphtory sandbox online, no installation needed.

 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Raphtory/Raphtory/master?labpath=examples%2Fbinder_python%2Findex.ipynb) 

Want to see what Raphtory can do? Scroll down for more.

#### 1. Getting started

| Type | Location  | Description |
| ------------- | ------------- | ------------- |
| Example | <a href="https://docs.raphtory.com/en/master/Introduction/ingestion.html">ingestion</a> | Loading some sample data into Raphtory |
| Example | <a href="https://docs.raphtory.com/en/master/Introduction/analysis.html#Simplest-Raphtory-Query">degree count</a> | Running the simplest graph query in Raphtory|
| Example | <a href="https://docs.raphtory.com/en/master/Introduction/analysis.html#Time-API-Overview">timetravel</a> | Understanding the time APIs in Raphtory |

#### 2. Running some algorithms 

| Type | Location  | Description |
| ------------- | ------------- | ------------- |
| Example | <a href="https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/centrality/index.html">centrality</a> | Centrality algorithms for finding important nodes |
| Example | <a href="">community</a> | Community detection for finding clusters |
| Example | <a href="https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/dynamic/index.html">dynamic</a> | Dynamic algorithms and random walks |
| Example | <a href="">temporal</a> | Applying time magic to find historic and future trends |

#### 3. Developing an end-to-end application

| Type | Location  | Description |
| ------------- | ------------- | ------------- |
| Notebook | <a href="https://github.com/Raphtory/Raphtory/blob/master/examples/nft/src/main/python/nft_analysis.ipynb">nft_analysis.ipynb</a> | Analysing pump and dump cycles of popular NFTs |
| Notebook | <a href="https://github.com/Raphtory/Raphtory/blob/master/examples/companies-house/src/main/python/PPEContractsAnalysisNotebook.ipynb">ppe_analysis.ipnyb</a>  | Fraud and COVID-19 Relief Schemes |

# Want to run your own analysis?
Learn how to use Raphtory in your analysis and project by following these links.

- **[Latest documentation](https://docs.raphtory.com/)**
- [Using Raphtory in 100 seconds](https://docs.raphtory.com/en/master/Introduction/ingestion.html)
- [Complete list of available algorithms](https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/index.html)
- [Writing your own algorithm in Raphtory](https://docs.raphtory.com/en/master/Analysis/LOTR_six_degrees.html)

# Bounty board

Raphtory is currently offering rewards for contributions, such as new features or algorithms. Contributors will receive swag and prizes! 

To get started, check out our list of desired algorithms at https://www.raphtory.com/algorithm-bounty/ which include some low hanging fruit (🍇) that are easy to implement. 


# Community  
Join the growing community of open-source enthusiasts using Raphtory to power their graph analysis projects!

- Follow [![Slack](https://img.shields.io/twitter/follow/raphtory?label=@raphtory)](https://twitter.com/raphtory) for the latest Raphtory news and development

- Join our [![Slack](https://img.shields.io/badge/community-Slack-red)](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA) to chat with us and get answers to your questions!


#### Articles and Talks about Raphtory
- **[Raphtory on the Alan Turing Institute Blog](https://www.turing.ac.uk/blog/just-add-time-dizzying-potential-dynamic-graphs)**
- **[Talk on Raphtory at AI UK 2022](https://www.youtube.com/watch?v=7S9Ymnih-YM&list=PLuD_SqLtxSdVEUsCYlb5XjWm9D6WuNKEz&index=9)**
- **[Talk on Raphtory at KGC 2022](https://www.youtube.com/watch?v=37S4bSN5EaU)**
- **[Talk on Raphtory at NetSciX 2022](https://www.youtube.com/watch?v=QxhrONca4FE)**


# Contributors

<a href="https://github.com/raphtory/raphtory/graphs/contributors"><img src="https://contrib.rocks/image?repo=raphtory/raphtory"/></a>

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).




            

Raw data

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    "requires_python": ">=3.9",
    "maintainer_email": "Pometry <admin@pometry.com>",
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    "author": "Fabian Murariu, Lucas Jeub, Ben Steer, Haaroon Yousaf",
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    "platform": null,
    "description": "<br>\n<p align=\"center\">\n  <img src=\"https://user-images.githubusercontent.com/6665739/130641943-fa7fcdb8-a0e7-4aa4-863f-3df61b5de775.png\" alt=\"Raphtory\" height=\"100\"/>\n</p>\n<p align=\"center\">\n</p>\n\n\n<p align=\"center\">\n<a href=\"https://github.com/Raphtory/Raphtory/actions/workflows/test.yml/badge.svg\">\n<img alt=\"Test and Build\" src=\"https://github.com/Raphtory/Raphtory/actions/workflows/test.yml/badge.svg\" />\n</a>\n<a href=\"https://github.com/Raphtory/Raphtory/releases\">\n<img alt=\"Latest Release\" src=\"https://img.shields.io/github/v/release/Raphtory/Raphtory?color=brightgreen&include_prereleases\" />\n</a>\n<a href=\"https://github.com/Raphtory/Raphtory/issues\">\n<img alt=\"Issues\" src=\"https://img.shields.io/github/issues/Raphtory/Raphtory?color=brightgreen\" />\n</a>\n<a href=\"https://mybinder.org/v2/gh/Raphtory/Raphtory/v0.1.6?labpath=examples%2Fbinder_python%2Findex.ipynb\">\n<img alt=\"Launch Notebook\" src=\"https://mybinder.org/badge_logo.svg\" />\n</a>\n</p>\n<p align=\"center\">\n<a href=\"https://www.raphtory.com\">\ud83c\udf0d Website </a>\n&nbsp\n<a href=\"https://docs.raphtory.com/\">\ud83d\udcd2 Documentation</a>\n&nbsp \n<a href=\"https://www.pometry.com\"><img src=\"https://user-images.githubusercontent.com/6665739/202438989-2859f8b8-30fb-4402-820a-563049e1fdb3.png\" height=\"20\" align=\"center\"/> Pometry</a> \n&nbsp\n<a href=\"https://docs.raphtory.com/en/master/Introduction/ingestion.html\">\ud83e\uddd9\ud83c\udffb\u200d Tutorial</a> \n&nbsp\n<a href=\"https://github.com/Raphtory/Raphtory/issues\">\ud83d\udc1b Report a Bug</a> \n&nbsp\n<a href=\"https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA\"><img src=\"https://user-images.githubusercontent.com/6665739/154071628-a55fb5f9-6994-4dcf-be03-401afc7d9ee0.png\" height=\"20\" align=\"center\"/> Join Slack</a> \n</p>\n\n<br>\n\nRaphtory is a powerful analytics engine for large-scale graph analysis. It lets you run complex queries on your data, no matter where it's stored or what format it's in. But that's not all - Raphtory's real superpower is its ability to track and explore the history of a complex system, from \"time traveling\" through data to executing advanced analysis like taint tracking, temporal reachability, and mining temporal motifs.\n\n**Raphtory is easy to use:** just run a single pip install command and embed it with your existing Python/Pandas pipeline for input and output.\n\n**Raphtory is expressive:** It's designed to represent all types of graph queries and has a well-developed API for exploring your data across its history.\n\n**Raphtory is lightning-fast and scales effortlessly**: Built on Apache Arrow's storage and vectorized compute, Raphtory can be run on a laptop or a distributed cluster for terabyte-scale graphs.\n\n# Running a basic example\n\n```python\n#\u00a0Import Raphtory\nimport PyRaphtory\n\n#\u00a0Create a new local or distributed context\nctx = PyRaphtory.local()\ngraph = ctx.new_graph()\n\n# Add some data to your graph\ngraph.add_vertex(1, 1)\ngraph.add_vertex(2, 2)\ngraph.add_vertex(3, 3)\ngraph.add_edge(4, 1, 2)\ngraph.add_edge(4, 1, 3)\n\n# Collect some simple vertex metrics\n#\u00a0Ran across a range of the data with incremental windowing\ndf = graph\n      .range(1,4,1)\n      .window(1)\n      .step(lambda vertex: vertex.set_state(\"name\", vertex.name()))\n      .step(lambda vertex: vertex.set_state(\"out_degree\", vertex.out_degree())) \n      .step(lambda vertex: vertex.set_state(\"in_degree\", vertex.in_degree()))\n      .select(\"name\", \"out_degree\", \"in_degree\")\n      .to_df()\n\n# Preview DataFrame\ndf\n\n|    |   timestamp |   window |   name |   out_degree |   in_degree |\n|----|-------------|----------|--------|--------------|-------------|\n|  0 |           1 |        1 |      1 |            0 |           0 |\n|  1 |           2 |        1 |      2 |            0 |           0 |\n|  2 |           3 |        1 |      3 |            0 |           0 |\n|  3 |           4 |        1 |      1 |            2 |           0 |\n|  4 |           4 |        1 |      2 |            0 |           1 |\n|  5 |           4 |        1 |      3 |            0 |           1 |\n```\n\n# Installing Raphtory \n\nRaphtory is available for Python and Scala/Java, with support for Rust planned in version 0.3.0. We recommend using the PyRaphtory client for Python, which includes everything you need and can be run locally or in distributed mode.\n\nYou should have Python version 3.9 or higher. It's a good idea to use conda, virtualenv, or pyenv. \n\n```bash\npip install pyraphtory\n``` \n\n# Examples and Notebooks\n\nCheck out Raphtory in action with our interactive Jupyter Notebook! Just click the badge below to launch a Raphtory sandbox online, no installation needed.\n\n [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Raphtory/Raphtory/master?labpath=examples%2Fbinder_python%2Findex.ipynb) \n\nWant to see what Raphtory can do? Scroll down for more.\n\n#### 1. Getting started\n\n| Type | Location  | Description |\n| ------------- | ------------- | ------------- |\n| Example | <a href=\"https://docs.raphtory.com/en/master/Introduction/ingestion.html\">ingestion</a> | Loading some sample data into Raphtory |\n| Example | <a href=\"https://docs.raphtory.com/en/master/Introduction/analysis.html#Simplest-Raphtory-Query\">degree count</a> | Running the simplest graph query in Raphtory|\n| Example | <a href=\"https://docs.raphtory.com/en/master/Introduction/analysis.html#Time-API-Overview\">timetravel</a> | Understanding the time APIs in Raphtory |\n\n#### 2. Running some algorithms \n\n| Type | Location  | Description |\n| ------------- | ------------- | ------------- |\n| Example | <a href=\"https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/centrality/index.html\">centrality</a> | Centrality algorithms for finding important nodes |\n| Example | <a href=\"\">community</a> | Community detection for finding clusters |\n| Example | <a href=\"https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/dynamic/index.html\">dynamic</a> | Dynamic algorithms and random walks |\n| Example | <a href=\"\">temporal</a> | Applying time magic to find historic and future trends |\n\n#### 3. Developing an end-to-end application\n\n| Type | Location  | Description |\n| ------------- | ------------- | ------------- |\n| Notebook | <a href=\"https://github.com/Raphtory/Raphtory/blob/master/examples/nft/src/main/python/nft_analysis.ipynb\">nft_analysis.ipynb</a> | Analysing pump and dump cycles of popular NFTs |\n| Notebook | <a href=\"https://github.com/Raphtory/Raphtory/blob/master/examples/companies-house/src/main/python/PPEContractsAnalysisNotebook.ipynb\">ppe_analysis.ipnyb</a>  | Fraud and COVID-19 Relief Schemes |\n\n# Want to run your own analysis?\nLearn how to use Raphtory in your analysis and project by following these links.\n\n- **[Latest documentation](https://docs.raphtory.com/)**\n- [Using Raphtory in 100 seconds](https://docs.raphtory.com/en/master/Introduction/ingestion.html)\n- [Complete list of available algorithms](https://docs.raphtory.com/en/master/_autodoc/com/raphtory/algorithms/generic/index.html)\n- [Writing your own algorithm in Raphtory](https://docs.raphtory.com/en/master/Analysis/LOTR_six_degrees.html)\n\n# Bounty board\n\nRaphtory is currently offering rewards for contributions, such as new features or algorithms. Contributors will receive swag and prizes! \n\nTo get started, check out our list of desired algorithms at https://www.raphtory.com/algorithm-bounty/ which include some low hanging fruit (\ud83c\udf47) that are easy to implement. \n\n\n# Community  \nJoin the growing community of open-source enthusiasts using Raphtory to power their graph analysis projects!\n\n- Follow [![Slack](https://img.shields.io/twitter/follow/raphtory?label=@raphtory)](https://twitter.com/raphtory) for the latest Raphtory news and development\n\n- Join our [![Slack](https://img.shields.io/badge/community-Slack-red)](https://join.slack.com/t/raphtory/shared_invite/zt-xbebws9j-VgPIFRleJFJBwmpf81tvxA) to chat with us and get answers to your questions!\n\n\n#### Articles and Talks about Raphtory\n- **[Raphtory on the Alan Turing Institute Blog](https://www.turing.ac.uk/blog/just-add-time-dizzying-potential-dynamic-graphs)**\n- **[Talk on Raphtory at AI UK 2022](https://www.youtube.com/watch?v=7S9Ymnih-YM&list=PLuD_SqLtxSdVEUsCYlb5XjWm9D6WuNKEz&index=9)**\n- **[Talk on Raphtory at KGC 2022](https://www.youtube.com/watch?v=37S4bSN5EaU)**\n- **[Talk on Raphtory at NetSciX 2022](https://www.youtube.com/watch?v=QxhrONca4FE)**\n\n\n# Contributors\n\n<a href=\"https://github.com/raphtory/raphtory/graphs/contributors\"><img src=\"https://contrib.rocks/image?repo=raphtory/raphtory\"/></a>\n\nWant 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!\n\n# License  \n\nRaphtory is licensed under the terms of the Apache License (check out our LICENSE file).\n\n\n\n",
    "bugtrack_url": null,
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