NeuroData's Graph DataBase

Lowering the barrier to entry for scalable neuroscience

Compute multivariate invariants on graphs

Specifications for acceptable input graphs

It should be loadable from the python shell via the following call:['your_data_element_name'].
Such a matrix can be created by creating an MATLAB matrix and using the save function or via Python's function.

Single graph

As long as your graph meets one of the above specifications you may compute invariants on any graph of your choosing.

Multiple graphs

To compute invariants for multiple graphs, with one click — upload a zipfile (.zip) with all the graphs in a single folder, fill the form below and submit.

Programmatic use

This is can be accessed using the base url
The example python script can be found here.

How to use the example

After downlaoding the example above, start a terminal session. Assuming you are in the same directory as the example. Typing python -h will give help on how to use the example and what options are available to you.

Single graph

Assume we have a single graph located in the same directory as the example script named GRAPH_FILE. Typing

python \
-i cc tri ss1
will upload your GRAPH_FILE file, in GRAPH_FORMAT format; compute: clustering coefficient, local triangle count, and scan statistic-1.

Here is a breakdown of the command line call arguments:

Starting a tmux or screen session is recommended as you must wait for the http response to otbain a url containing the result.