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Graph database wrapper for non-graph datastores

Project description

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Graph toolkit interoperability and scalability for Python

Installation

pip install grand-graph

Example use-cases

  • Write NetworkX commands to analyze true-serverless graph databases using DynamoDB*
  • Query a host graph in SQL for subgraph isomorphisms with DotMotif
  • Write iGraph code to construct a graph, and then play with it in Networkit
  • Attach node and edge attributes to Networkit or IGraph graphs

* Neptune is not true-serverless.

Why it's a big deal

Grand is a Rosetta Stone of graph technologies. A Grand graph has a "Backend," which handles the implementation-details of talking to data on disk (or in the cloud), and an "Dialect", which is your preferred way of talking to a graph.

For example, here's how you make a graph that is persisted in DynamoDB (the "Backend") but that you can talk to as though it's a networkx.DiGraph (the "Dialect"):

import grand

G = grand.Graph(backend=grand.DynamoDBBackend())

G.nx.add_node("Jordan", type="Person")
G.nx.add_node("DotMotif", type="Project")

G.nx.add_edge("Jordan", "DotMotif", type="Created")

assert len(G.nx.edges()) == 1
assert len(G.nx.nodes()) == 2

It doesn't stop there. If you like the way IGraph handles anonymous node insertion (ugh) but you want to handle the graph using regular NetworkX syntax, use a IGraphDialect and then switch to a NetworkXDialect halfway through:

import grand

G = grand.Graph()

# Start in igraph:
G.igraph.add_vertices(5)

# A little bit of networkit:
G.networkit.addNode()

# And switch to networkx:
assert len(G.nx.nodes()) == 6

# And back to igraph!
assert len(G.igraph.vs) == 6

You should be able to use the "dialect" objects the same way you'd use a real graph from the constituent libraries. For example, here is a NetworkX algorithm running on NetworkX graphs alongside Grand graphs:

import networkx as nx

nx.algorithms.isomorphism.GraphMatcher(networkxGraph, grandGraph.nx)

Here is an example of using Networkit, a highly performant graph library, and attaching node/edge attributes, which are not supported by the library by default:

import grand
from grand.backends.networkit import NetworkitBackend

G = grand.Graph(backend=NetworkitBackend())

G.nx.add_node("Jordan", type="Person")
G.nx.add_node("Grand", type="Software")
G.nx.add_edge("Jordan", "Grand", weight=1)

print(G.nx.edges(data=True)) # contains attributes, even though graph is stored in networkit

Current Support

✅ = Fully Implemented 🤔 = In Progress 🔴 = Unsupported
Dialect Description & Notes Status
IGraphDialect Python-IGraph interface
NetworkXDialect NetworkX-like interface
NetworkitDialect Networkit-like interface
Backend Description & Notes Status
DataFrameBackend Stored in pandas-like tables
DynamoDBBackend Edge/node tables in DynamoDB
GremlinBackend For Gremlin datastores
IGraphBackend An IGraph graph, in memory
NetworkitBackend A Networkit graph, in memory
NetworkXBackend A NetworkX graph, in memory
SQLBackend Two SQL-queryable tables

You can read more about usage and learn about backends and dialects in the wiki.

Citing

If this tool is helpful to your research, please consider citing it with:

# https://doi.org/10.1038/s41598-021-91025-5
@article{Matelsky_Motifs_2021,
    title={{DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries}},
    volume={11},
    ISSN={2045-2322},
    url={http://dx.doi.org/10.1038/s41598-021-91025-5},
    DOI={10.1038/s41598-021-91025-5},
    number={1},
    journal={Scientific Reports},
    publisher={Springer Science and Business Media LLC},
    author={Matelsky, Jordan K. and Reilly, Elizabeth P. and Johnson, Erik C. and Stiso, Jennifer and Bassett, Danielle S. and Wester, Brock A. and Gray-Roncal, William},
    year={2021},
    month={Jun}
}

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