{"id":508,"date":"2018-05-29T08:24:48","date_gmt":"2018-05-29T07:24:48","guid":{"rendered":"http:\/\/pages.di.unipi.it\/bacciu\/?page_id=508"},"modified":"2019-12-30T11:57:52","modified_gmt":"2019-12-30T10:57:52","slug":"cgmm","status":"publish","type":"page","link":"https:\/\/pages.di.unipi.it\/bacciu\/software\/cgmm\/","title":{"rendered":"CGMM"},"content":{"rendered":"<h2>Contextual Graph Markov Model<\/h2>\n<p>The official Python implementation for the Contextual Graph Markov Model (CGMM), a deep and generative approach for learning unsupervised encoding of graphs, with a supervised wrapping mechanism for performing graph classification and\/or regression.<\/p>\n<p>The code is maintained in the Github of my student <a href=\"https:\/\/github.com\/diningphil\">Federico Errica<\/a>, who is to be credited for the implementation. To download the code and the scripts necessary to replicate the experiments in the original paper describing the model, please go <a href=\"https:\/\/github.com\/diningphil\/CGMM\">here<\/a>.<\/p>\n<p>The code is provided as is with no warranty and technical support. Please inform the authors of the original paper (details below) if you intend to redistribute the code.<\/p>\n<h2><strong>Citation<\/strong><\/h2>\n<p>If you find this code useful, please remember to cite:<\/p>\n<div class=\"tp_single_publication\"><span class=\"tp_single_author\">Bacciu Davide, Errica Federico, Micheli Alessio: <\/span> <span class=\"tp_single_title\">Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing<\/span>. <span class=\"tp_single_additional\"><span class=\"tp_pub_additional_booktitle\">Proceedings of the 35th International Conference on Machine Learning (ICML 2018), <\/span><span class=\"tp_pub_additional_year\">2018<\/span>.<\/span><\/div>\n<h2 class=\"tp_bibtex\">BibTeX (<a href=\"https:\/\/pages.di.unipi.it\/bacciu?feed=tp_pub_bibtex&amp;key=icml2018\">Download<\/a>)<\/h2><pre class=\"tp_bibtex\">@conference{icml2018,\r\ntitle = {Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing},\r\nauthor = {Bacciu Davide and Errica Federico and Micheli Alessio},\r\nurl = {https:\/\/arxiv.org\/abs\/1805.10636},\r\nyear  = {2018},\r\ndate = {2018-07-11},\r\nurldate = {2018-07-11},\r\nbooktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML 2018)},\r\nkeywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing},\r\npubstate = {published},\r\ntppubtype = {conference}\r\n}\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Contextual Graph Markov Model The official Python implementation for the Contextual Graph Markov Model (CGMM), a deep and generative approach for learning unsupervised encoding of graphs, with a supervised wrapping mechanism for performing graph classification and\/or regression. The code is maintained in the Github of my student Federico Errica, who is to be credited for [&hellip;]<\/p>\n","protected":false},"author":19,"featured_media":0,"parent":63,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-508","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/508","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/comments?post=508"}],"version-history":[{"count":4,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/508\/revisions"}],"predecessor-version":[{"id":769,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/508\/revisions\/769"}],"up":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/63"}],"wp:attachment":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/media?parent=508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}