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Graph database wrapper for non-graph datastores

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Graph toolkit interoperability and scalability for Python


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:

# A little bit of networkit:

# 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.


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

    title={{DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries}},
    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},

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