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Find graph motifs using simple, intuitive notation

Project description

d o t m o t i f

Find graph motifs using intuitive notation

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DotMotif is a library that identifies subgraphs or motifs in a large graph. It looks like this:

# Look for all motifs of the form,

# Neuron A synapses on Neuron B:
A -> B
# ...and B inhibits C:
B -> C [type = "inhibitory"]

Examples

Notebook Description
Open In Colab Looking for motifs in the IARPA MICrONS Pinky100 Dataset
Open In Colab Motif search in a custom graph
Open In Colab Subgraph search in the Janelia Hemibrain dataset

Get Started

If you have DotMotif, a NetworkX graph, and a curious mind, you already have everything you need to start using DotMotif:

from dotmotif import Motif, GrandIsoExecutor

executor = GrandIsoExecutor(graph=my_networkx_graph)

triangle = Motif("""
A -> B
B -> C
C -> A
""")

results = executor.find(triangle)

Parameters

You can also pass optional parameters into the constructor for the dotmotif object. Those arguments are:

Argument Type, Default Behavior
ignore_direction bool: False Whether to disregard direction when generating the database query
limit int: None A limit (if any) to impose on the query results
enforce_inequality bool: False Whether to enforce inequality; in other words, whether two nodes should be permitted to be aliases for the same node. For example, in A->B->C; if A!=C, then set to True
exclude_automorphisms bool: False Whether to return only a single example for each detected automorphism. See more in the documentation

For more details on how to write a query, see Getting Started.


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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