How to use `graynet` results ---------------------------- For each combination of the parameters chosen, such as edge metric, atlas etc, ``graynet`` produces one network for each subject. The output format is in ``GraphML`` format, which can be easily read with ``networkx``: .. code-block:: python import networkx as nx graph = nx.read_graphml(path_to_graphml_file) More info can be found `here `_. The graph inside the ``graphML`` file is essentially a pair-wise distance matrix (measured by the metric chosen). There are many ways you can use it - easiest among them is to extract the upper triangular part of the connectivity matrix (as it is symmetric) and treat it a single-subject feature vector for that subject. These subject-wise feature vectors can be used in many applications, including in the study of brain-behaviour relationships and as a biomarker candidate (e.g. see `this study on ADNI and ABIDE `_). If you are interested in evaluating their predictive utility (out of sample prediction via cross-validation), it's quite simple via `neuropredict `_. We encourage you to adopt ``graphML`` format to store and analyze the networks extracted from graynet, however, there maybe simple use cases you prefer simpler CSVs. To convert `GraphML` files to CSV format (containing just weight values, and nothing else), use the script https://github.com/raamana/graynet/blob/master/scripts/convert_graphml_to_csv.py We plan to include additional scripts and convenience methods into ``graynet`` to gather the results into readily usable data structures such as `pyradigm `_ for further analysis. Stay tuned!