{"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/multi-hop-assortativities-for-networks","title":"Multi-hop assortativities for networks classification","arxiv_id":"1809.06253","date":"2018-09-14","proceeding":null,"authors":["Leonardo Gutierrez Gomez","Jean-Charles Delvenne"],"abstract":"Several social, medical, engineering and biological challenges rely on\ndiscovering the functionality of networks from their structure and node\nmetadata, when it is available. For example, in chemoinformatics one might want\nto detect whether a molecule is toxic based on structure and atomic types, or\ndiscover the research field of a scientific collaboration network. Existing\ntechniques rely on counting or measuring structural patterns that are known to\nshow large variations from network to network, such as the number of triangles,\nor the assortativity of node metadata. We introduce the concept of multi-hop\nassortativity, that captures the similarity of the nodes situated at the\nextremities of a randomly selected path of a given length. We show that\nmulti-hop assortativity unifies various existing concepts and offers a\nversatile family of 'fingerprints' to characterize networks. These fingerprints\nallow in turn to recover the functionalities of a network, with the help of the\nmachine learning toolbox. Our method is evaluated empirically on established\nsocial and chemoinformatic network benchmarks. Results reveal that our\nassortativity based features are competitive providing highly accurate results\noften outperforming state of the art methods for the network classification\ntask.","url_abs":"http://arxiv.org/abs/1809.06253v2","url_pdf":"http://arxiv.org/pdf/1809.06253v2.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":"multi-hop-assortativities-for-networks","repo_url":"https://github.com/leoguti85/MaF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}