{"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/stochastic-graphlet-embedding","title":"Stochastic Graphlet Embedding","arxiv_id":"1702.00156","date":"2017-02-01","proceeding":null,"authors":["Anjan Dutta","Hichem Sahbi"],"abstract":"Graph-based methods are known to be successful in many machine learning and\npattern classification tasks. These methods consider semi-structured data as\ngraphs where nodes correspond to primitives (parts, interest points, segments,\netc.) and edges characterize the relationships between these primitives.\nHowever, these non-vectorial graph data cannot be straightforwardly plugged\ninto off-the-shelf machine learning algorithms without a preliminary step of --\nexplicit/implicit -- graph vectorization and embedding. This embedding process\nshould be resilient to intra-class graph variations while being highly\ndiscriminant. In this paper, we propose a novel high-order stochastic graphlet\nembedding (SGE) that maps graphs into vector spaces. Our main contribution\nincludes a new stochastic search procedure that efficiently parses a given\ngraph and extracts/samples unlimitedly high-order graphlets. We consider these\ngraphlets, with increasing orders, to model local primitives as well as their\nincreasingly complex interactions. In order to build our graph representation,\nwe measure the distribution of these graphlets into a given graph, using\nparticular hash functions that efficiently assign sampled graphlets into\nisomorphic sets with a very low probability of collision. When combined with\nmaximum margin classifiers, these graphlet-based representations have positive\nimpact on the performance of pattern comparison and recognition as corroborated\nthrough extensive experiments using standard benchmark databases.","url_abs":"http://arxiv.org/abs/1702.00156v3","url_pdf":"http://arxiv.org/pdf/1702.00156v3.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":"stochastic-graphlet-embedding","repo_url":"https://github.com/priba/hierarchicalSGE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.00156","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}