[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116952-en":3,"doc-seo-116952-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116952,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",6,"Technology","PHOTONAI Graph - a Python Toolbox for Graph Machine Learning","PHOTONAI Graph addresses the gap between domain experts in machine learning, graph specialists, and neuroscientists by providing an easy-to-use Python toolbox built on the PHOTONAI machine learning API. It enables the design, optimization, and evaluation of reliable graph machine learning pipelines with hyperparameter optimization, algorithm evaluation, and reproducible performance estimates. The toolbox supports graph neural networks, graph embeddings, and graph kernels, enabling complex modeling with minimal coding. Example pipelines for resting-state fMRI and DTI illustrate its versatility and promote adoption in neuroscience.","medRxiv preprint doi: [https://doi.org/10.1101/2023.06.22.23291748](https://doi.org/10.1101/2023.06.22.23291748); this version posted June 29, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY-NC-ND 4.0 International license .  \nPHOTONAI-Graph-A Python Toolbox for Graph Machine Learning  \nJan Ernsting* 1, 2, 3 , Vincent Holstein*2,􀀀 , Nils R. Winter2 , Kelvin Sarink2 , Ramona Leenings2, 3 , Marius Gruber2, 4 , Jonathan,  \nRepple2, 4 , Benjamin Risse 1, 3 , Udo Dannlowski2 , and Tim Hahn2  \n1 Institute for Geoinformatics, University of Münster, Germany  \n2 Institute for Translational Psychiatry, University of Münster, Germany  \n3 Faculty of Mathematics and Computer Science, University of Münster, Germany  \n4 Department of Psychiatry, Psychosomatics and Psychotherapy, University of Frankfurt, Germany *  \nThese authors contributed equally: Jan Ernsting, Vincent Holstein  \nGraph 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.  \nGraph Machine Learning | Network Neuroscience | Graph Neural Networks | Auto-ML  \nCorrespondence: v_[hols01@uni-muenster.de](hols01@uni-muenster.de)  \nIntroduction  \nGraph data is ubiquitous throughout biomedical research and can be found in many different fields. In neuroscience, graph representations are of particular interest as the neuronal connections within the brain are a naturally occurring graph structure. These graphs arise both on the microscopic level in cellular connection networks and the macroscopic level such as higher-order brain circuits. Functional connectivity and diffusion tensor imaging (DTI) are the two most commonly used modalities for studying these circuits in vivo. The two modalities allow for the construction of brain connectivity graphs, which can then be studied using graph theoretical approaches. Different established toolboxes allow neuroscientists to apply classical graph theoretical approaches using statistical analysis. However, multivariate graph analyses such as graph machine learning pipelines are mostly limited to a domain expert group, limiting the accessibility for many neu-  \nroscientists.  \nIn classical graph analysis graph properties are calculated, sometimes referred to as graph measures, which are then analyzed using statistical analysis tools such as general linear models (GLMs) . This approach has been increasingly adopted in neuroimaging, which is in part due to the availability of toolboxes, that support these analyses [1, 2] . These toolboxes a","cbCaihwVfszetmw9","https://ap.wps.com/l/cbCaihwVfszetmw9","pdf",1620396,1,10,"English","en",105,"# Introduction\n## Graph data in biomedical research and neuroscience\n## Classical graph analysis versus graph machine learning\n## Existing toolboxes and their limitations\n## PHOTONAI Graph and graph ML methods","[{\"question\":\"What problem does PHOTONAI Graph aim to solve?\",\"answer\":\"It targets the limited availability of easy-to-use software for applying graph-specific machine learning to graph modalities common in neuroscience.\"},{\"question\":\"Which graph machine learning approaches does PHOTONAI Graph integrate?\",\"answer\":\"It supports established methods such as graph neural networks, graph embeddings, and graph kernels.\"},{\"question\":\"How does PHOTONAI Graph help ensure reliable and reproducible results?\",\"answer\":\"It provides access to custom graph ML pipelines with hyperparameter optimization and algorithm evaluation, designed to yield valid performance estimates.\"}]","PHOTONAI Graph - a Python Toolbox for Graph Machine Learning | PDF",1785672809,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"photonai-graph-a-python-toolbox-for-graph-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/photonai-graph-a-python-toolbox-for-graph-machine-learning/116952/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does PHOTONAI Graph aim to solve?","Question",{"text":75,"@type":76},"It targets the limited availability of easy-to-use software for applying graph-specific machine learning to graph modalities common in neuroscience.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which graph machine learning approaches does PHOTONAI Graph integrate?",{"text":80,"@type":76},"It supports established methods such as graph neural networks, graph embeddings, and graph kernels.",{"name":82,"@type":73,"acceptedAnswer":83},"How does PHOTONAI Graph help ensure reliable and reproducible results?",{"text":84,"@type":76},"It provides access to custom graph ML pipelines with hyperparameter optimization and algorithm evaluation, designed to yield valid performance estimates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]