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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.
```python
import PyRaphtory
ctx = PyRaphtory.local() graph = ctx.new_graph()
graph.addvertex(1, 1) graph.addvertex(2, 2) graph.addvertex(3, 3) graph.addedge(4, 1, 2) graph.add_edge(4, 1, 3)
df = graph .range(1,4,1) .window(1) .step(lambda vertex: vertex.setstate("name", vertex.name())) .step(lambda vertex: vertex.setstate("outdegree", vertex.outdegree())) .step(lambda vertex: vertex.setstate("indegree", vertex.indegree())) .select("name", "outdegree", "indegree") .todf()
df
| | timestamp | window | name | outdegree | indegree | |----|-------------|----------|--------|--------------|-------------| | 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 | ```
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
Check out Raphtory in action with our interactive Jupyter Notebook! Just click the badge below to launch a Raphtory sandbox online, no installation needed.
Want to see what Raphtory can do? Scroll down for more.
| Type | Location | Description | | ------------- | ------------- | ------------- | | Example | ingestion | Loading some sample data into Raphtory | | Example | degree count | Running the simplest graph query in Raphtory| | Example | timetravel | Understanding the time APIs in Raphtory |
| Type | Location | Description | | ------------- | ------------- | ------------- | | Example | centrality | Centrality algorithms for finding important nodes | | Example | community | Community detection for finding clusters | | Example | dynamic | Dynamic algorithms and random walks | | Example | temporal | Applying time magic to find historic and future trends |
| Type | Location | Description | | ------------- | ------------- | ------------- | | Notebook | nftanalysis.ipynb | Analysing pump and dump cycles of popular NFTs | | Notebook | ppeanalysis.ipnyb | Fraud and COVID-19 Relief Schemes |
Learn how to use Raphtory in your analysis and project by following these links.
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.
Join the growing community of open-source enthusiasts using Raphtory to power their graph analysis projects!
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Want to get involved? Please join the Raphtory Slack group and speak with us on how you could pitch in!
Raphtory is licensed under the terms of the Apache License (check out our LICENSE file).