PHOTONAI-Graph - A Python Toolbox for Graph Machine Learning

Graph data is an omnipresent way to represent information in machine learning. Especially, in neuroscience research, data from Diffusion-Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) is commonly represented as graphs. Exploiting the graph structure of these modalities using graph-specific machine learning applications is currently hampered by the lack of easy-to-use software. PHOTONAI Graph aims to close the gap between domain experts of machine learning, graph experts and neuroscientists. Leveraging the rapid machine learning model development features of the Python machine learning API PHOTONAI, PHOTONAI Graph enables the design, optimization, and evaluation of reliable graph machine learning models for practitioners. As such, it provides easy access to custom graph machine learning pipelines including, hyperparameter optimization and algorithm evaluation ensuring reproducibility and valid performance estimates. Integrating established algorithms such as graph neural networks, graph embeddings and graph kernels, it allows researchers without significant coding experience to build and optimize complex graph machine learning models within a few lines of code. We showcase the versatility of this toolbox by building pipelines for both resting–state fMRI and DTI data in the hope that it will increase the adoption of graph-specific machine learning algorithms in neuroscience research. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by grants from the Interdisciplinary Center for Clinical Research (IZKF, https://www.medizin.uni-muenster.de/izkf.html) of the medical faculty of Muenster (grant MzH 3/020/20 to TH and grant Dan3/012/17 to UD) and the German Research Foundation (DFG, https://www.dfg.de/, grants HA7070/2-2, HA7070/3, HA7070/4 to TH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used only data that was publicly available from the Human Connectome Project (https://www.humanconnectome.org/study/hcp-young-adult/overview) and the 10KIn1Day Dataset (http://www.dutchconnectomelab.nl/10Kdata/). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used in the study are available at https://www.humanconnectome.org/study/hcp-young-adult/overview (https://db.humanconnectome.org/data/HCP\_1200) and http://www.dutchconnectomelab.nl/10Kdata/. All code used in the analysis is publicly available (https://github.com/wwu-mmll/photonai\_graph\_usecases; https://github.com/wwu-mmll/photonai\_graph).