{"id":3607,"date":"2025-11-05T01:08:43","date_gmt":"2025-11-05T01:08:43","guid":{"rendered":"https:\/\/blogs.qub.ac.uk\/dipsa\/?p=3607"},"modified":"2026-04-30T16:22:20","modified_gmt":"2026-04-30T15:22:20","slug":"orbitsi-is-now-on-pypi","status":"publish","type":"post","link":"https:\/\/blogs.qub.ac.uk\/dipsa\/orbitsi-is-now-on-pypi\/","title":{"rendered":"OrbitSI is now on PyPi"},"content":{"rendered":"\n<p><strong>OrbitSI<\/strong> is an open-source Python framework designed to efficiently solve the <em>subgraph isomorphism enumeration<\/em> problem, i.e., identifying all subgraphs within a data graph that are structurally identical to a given pattern graph. The tool introduces an orbit-aware pruning and ordering strategy that significantly improves enumeration speed compared to classical algorithms. OrbitSI enhances computational performance by integrating structural information about node roles, referred to as <em>orbits<\/em>, to prune the search space before enumeration. It is built atop NetworkX and C++ backends such as <strong>EVOKE<\/strong> and <strong>ORCA<\/strong>. The framework supports both command-line and Python interfaces, enabling researchers and practitioners to perform subgraph searches and orbit counting tasks with ease. It is distributed under the Apache 2.0 License.<\/p>\n\n\n\n<p>Check out OrbitSI on Github: <a href=\"https:\/\/github.com\/ibtisamtauhidi\/OrbitSI\">https:\/\/github.com\/ibtisamtauhidi\/OrbitSI<\/a><\/p>\n\n\n\n<p>Install through PyPi: <a href=\"https:\/\/pypi.org\/project\/orbitsi\/\">https:\/\/pypi.org\/project\/orbitsi\/<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>OrbitSI is an open-source Python framework designed to efficiently solve the subgraph isomorphism enumeration problem, i.e., identifying all subgraphs within a data graph that are structurally identical to a given pattern graph. The tool introduces an orbit-aware pruning and ordering strategy that significantly improves enumeration speed compared to classical algorithms. OrbitSI enhances computational performance by [&hellip;]<\/p>\n","protected":false},"author":1149,"featured_media":3632,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[69],"tags":[35,38,19],"class_list":{"0":"post-3607","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-subgraph-isomorphism","8":"tag-graph-processing","9":"tag-high-performance-computing","10":"tag-source-code","11":"czr-hentry"},"jetpack_featured_media_url":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-content\/uploads\/sites\/14\/2024\/12\/Screenshot-from-2025-11-09-11-46-42.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/posts\/3607","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/users\/1149"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/comments?post=3607"}],"version-history":[{"count":1,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/posts\/3607\/revisions"}],"predecessor-version":[{"id":3608,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/posts\/3607\/revisions\/3608"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/media\/3632"}],"wp:attachment":[{"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/media?parent=3607"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/categories?post=3607"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.qub.ac.uk\/dipsa\/wp-json\/wp\/v2\/tags?post=3607"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}