{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/network-classification-with-applications-to","title":"Network classification with applications to brain connectomics","arxiv_id":"1701.08140","date":"2017-01-27","proceeding":null,"authors":["Jesús D. Arroyo-Relión","Daniel Kessler","Elizaveta Levina","Stephan F. Taylor"],"abstract":"While statistical analysis of a single network has received a lot of\nattention in recent years, with a focus on social networks, analysis of a\nsample of networks presents its own challenges which require a different set of\nanalytic tools. Here we study the problem of classification of networks with\nlabeled nodes, motivated by applications in neuroimaging. Brain networks are\nconstructed from imaging data to represent functional connectivity between\nregions of the brain, and previous work has shown the potential of such\nnetworks to distinguish between various brain disorders, giving rise to a\nnetwork classification problem. Existing approaches tend to either treat all\nedge weights as a long vector, ignoring the network structure, or focus on\ngraph topology as represented by summary measures while ignoring the edge\nweights. Our goal is to design a classification method that uses both the\nindividual edge information and the network structure of the data in a\ncomputationally efficient way, and that can produce a parsimonious and\ninterpretable representation of differences in brain connectivity patterns\nbetween classes. We propose a graph classification method that uses edge\nweights as predictors but incorporates the network nature of the data via\npenalties that promote sparsity in the number of nodes, in addition to the\nusual sparsity penalties that encourage selection of edges. We implement the\nmethod via efficient convex optimization and provide a detailed analysis of\ndata from two fMRI studies of schizophrenia.","url_abs":"http://arxiv.org/abs/1701.08140v3","url_pdf":"http://arxiv.org/pdf/1701.08140v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"network-classification-with-applications-to","repo_url":"https://github.com/jesusdaniel/graphclass","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":null,"task_name":"Functional Connectivity"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.08140","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}