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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