{"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-structure-metadata-and-the-prediction","title":"Network structure, metadata and the prediction of missing nodes and annotations","arxiv_id":"1604.00255","date":"2016-04-01","proceeding":null,"authors":["Darko Hric","Tiago P. Peixoto","Santo Fortunato"],"abstract":"The empirical validation of community detection methods is often based on\navailable annotations on the nodes that serve as putative indicators of the\nlarge-scale network structure. Most often, the suitability of the annotations\nas topological descriptors itself is not assessed, and without this it is not\npossible to ultimately distinguish between actual shortcomings of the community\ndetection algorithms on one hand, and the incompleteness, inaccuracy or\nstructured nature of the data annotations themselves on the other. In this work\nwe present a principled method to access both aspects simultaneously. We\nconstruct a joint generative model for the data and metadata, and a\nnonparametric Bayesian framework to infer its parameters from annotated\ndatasets. We assess the quality of the metadata not according to its direct\nalignment with the network communities, but rather in its capacity to predict\nthe placement of edges in the network. We also show how this feature can be\nused to predict the connections to missing nodes when only the metadata is\navailable, as well as missing metadata. By investigating a wide range of\ndatasets, we show that while there are seldom exact agreements between metadata\ntokens and the inferred data groups, the metadata is often informative of the\nnetwork structure nevertheless, and can improve the prediction of missing\nnodes. This shows that the method uncovers meaningful patterns in both the data\nand metadata, without requiring or expecting a perfect agreement between the\ntwo.","url_abs":"http://arxiv.org/abs/1604.00255v2","url_pdf":"http://arxiv.org/pdf/1604.00255v2.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-structure-metadata-and-the-prediction","repo_url":"https://git.skewed.de/count0/graph-tool","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}