raphtory 0.0.11

Python package for DocBrown, a temporal graph library

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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.

Running a basic example

# 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.

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.

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

1. Getting started

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

2. Running some algorithms

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

3. Developing an end-to-end application

Type	Location	Description
Notebook	nft_analysis.ipynb	Analysing pump and dump cycles of popular NFTs
Notebook	ppe_analysis.ipnyb	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
• Using Raphtory in 100 seconds
• Complete list of available algorithms
• Writing your own algorithm in Raphtory

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 for the latest Raphtory news and development

• Join our to chat with us and get answers to your questions!

Articles and Talks about Raphtory

• Raphtory on the Alan Turing Institute Blog
• Talk on Raphtory at AI UK 2022
• Talk on Raphtory at KGC 2022
• Talk on Raphtory at NetSciX 2022

Contributors

Want to get involved? Please join the Raphtory Slack 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).