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Raphtory is an in-memory vectorised graph database written in Rust with friendly Python APIs on top. It is blazingly fast, scales to hundreds of millions of edges on your laptop, and can be dropped into your existing pipelines with a simple pip install raphtory.

It supports time traveling, full-text search, multilayer modelling, and advanced analytics beyond simple querying like automatic risk detection, dynamic scoring, and temporal motifs.

If you wish to contribute, check out the open list of issues, bounty board or hit us up directly on slack. Successful contributions will be reward with swizzling swag!

Running a basic example

from raphtory import Graph
from raphtory import algorithms as algo
import pandas as pd

# Create a new graph
graph = Graph()

# Add some data to your graph
graph.add_vertex(timestamp=1, id="Alice")
graph.add_vertex(timestamp=1, id="Bob")
graph.add_vertex(timestamp=1, id="Charlie")
graph.add_edge  (timestamp=2, src="Bob",   dst="Charlie", properties={"weight":5.0})
graph.add_edge  (timestamp=3, src="Alice", dst="Bob",     properties={"weight":10.0})
graph.add_edge  (timestamp=3, src="Bob",   dst="Charlie", properties={"weight":-15.0})

# Check the number of unique nodes/edges in the graph and earliest/latest time seen.
print(graph)

results = [["earliest_time", "name", "out_degree", "in_degree"]]

# Collect some simple vertex metrics Ran across the history of your graph with a rolling window
for graph_view in graph.rolling(window=1):
    for v in graph_view.vertices():
        results.append([graph_view.earliest_time(), v.name(), v.out_degree(), v.in_degree()])

# Print the results
print(pd.DataFrame(results[1:], columns=results[0]))

# Grab an edge, explore the history of its 'weight' 
cb_edge = graph.edge("Bob","Charlie")
weight_history = cb_edge.properties.temporal.get("weight").items()
print("The edge between Bob and Charlie has the following weight history:", weight_history)

# Compare this weight between time 2 and time 3
weight_change = cb_edge.at(2)["weight"] - cb_edge.at(3)["weight"]
print("The weight of the edge between Bob and Charlie has changed by",weight_change,"pts")

# Run pagerank and ask for the top ranked node
top_node = algo.pagerank(graph).top_k(1)
print("The most important node in the graph is",top_node[0][0],"with a score of",top_node[0][1])

Graph(number_of_edges=2, number_of_vertices=3, earliest_time=1, latest_time=3)

|   | earliest_time | name    | out_degree | in_degree |
|---|---------------|---------|------------|-----------|
| 0 | 1             | Alice   | 0          | 0         |
| 1 | 1             | Bob     | 0          | 0         |
| 2 | 1             | Charlie | 0          | 0         |
| 3 | 2             | Bob     | 1          | 0         |
| 4 | 2             | Charlie | 0          | 1         |
| 5 | 3             | Alice   | 1          | 0         |
| 6 | 3             | Bob     | 1          | 1         |
| 7 | 3             | Charlie | 0          | 1         |

The edge between Bob and Charlie has the following weight history: [(2, 5.0), (3, -15.0)]

The weight of the edge between Bob and Charlie has changed by 20.0 pts

The top node in the graph is Charlie with a score of 0.4744116163405977

GraphQL

Create/Load a graph

Save a raphtory graph and set the GRAPH_DIRECTORY environment variable to point to the directory containing the graph.

Alternatively you can run the code below to generate a graph.

mkdir -p /tmp/graphs
mkdir -p examples/rust/src/bin/lotr/data/
tail -n +2 resource/lotr.csv > examples/rust/src/bin/lotr/data/lotr.csv

cd examples/rust && cargo run --bin lotr -r

cp examples/rust/src/bin/lotr/data/graphdb.bincode /tmp/graphs/lotr.bincode

Run the GraphQL server

The code below will run GraphQL with a UI at localhost:1736

GraphlQL will look for graph files in /tmp/graphs or in the path set in the GRAPH_DIRECTORY Environment variable.

cd raphtory-graphql && cargo run -r

ℹ️Warning: Server must have the same version + environment

The GraphQL server must be running in the same environment (i.e. debug or release) and same Raphtory version as the generated graph, otherwise it will throw errors due to incompatible graph metadata across versions.

Following will be output upon a successful launch

warning: `raphtory` (lib) generated 17 warnings (run `cargo fix --lib -p raphtory` to apply 13 suggestions)
    Finished release [optimized] target(s) in 0.91s
     Running `Raphtory/target/release/raphtory-graphql`
loading graph from /tmp/graphs/lotr.bincode
Playground: http://localhost:1736
  2023-08-11T14:36:52.444203Z  INFO poem::server: listening, addr: socket://0.0.0.0:1736
    at /Users/pometry/.cargo/registry/src/github.com-1ecc6299db9ec823/poem-1.3.56/src/server.rs:109

  2023-08-11T14:36:52.444257Z  INFO poem::server: server started
    at /Users/pometry/.cargo/registry/src/github.com-1ecc6299db9ec823/poem-1.3.56/src/server.rs:111

Execute a query

Go to the Playground at http://localhost:1736 and execute the following commands:

Query:

    query GetNodes($graphName: String!) {
        graph(name: $graphName) {
            nodes {
              name
            }
      }
    }

Query Variables:

{
  "graphName": "lotr.bincode"
}

Expected Result:

{
  "data": {
    "graph": {
      "nodes": [
        {
          "name": "Gandalf"
        },
        {
          "name": "Elrond"
        },
        {
          "name": "Frodo"
        },
        {
          "name": "Bilbo"
        },
        ...

Installing Raphtory

Raphtory is available for Python and Rust as of version 0.3.0. You should have Python version 3.10 or higher and it's a good idea to use conda, virtualenv, or pyenv.

pip install raphtory

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 give Raphtory a go on your laptop? You can checkout out the latest documentation and complete list of available algorithms or hop on our notebook based tutorials below!

Getting started

Type	Description
Tutorial	Building your first graph

Developing an end-to-end application

Type	Description
Notebook	Use our powerful time APIs to find pump and dump scams in popular NFTs

Benchmarks

We host a page which triggers and saves the result of two benchmarks upon every push to the master branch.

View this here https://pometry.github.io/Raphtory/dev/bench/

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://github.com/Raphtory/Raphtory/discussions/categories/bounty-board 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!

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 GNU General Public License v3.0 (check out our LICENSE file).

sgoggins/Raphtory