{"paper":{"title":"Graph distances for determining entities relationships: a topological approach to fraud detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"A. Ferrer-Sapena, E.A. S\\'anchez-P\\'erez, H. Falciani, J.M. Calabuig, L.M. Garc\\'ia-Raffi","submitted_at":"2019-05-03T14:02:38Z","abstract_excerpt":"Given a set $\\Omega$ and a proximity function $\\phi: \\Omega \\times \\Omega \\to \\mathbb R^+$, we define a new metric for $\\Omega$ by considering a path distance in $\\Omega$, that is considered as a complete graph. We analyze the properties of such a distance, and several procedures for defining the initial proximity matrix $( \\phi(a,b) )_{(a,b) \\in \\Omega \\times \\Omega}.$ Our motivation has its roots in the current interest in finding effective algorithms for detecting and classifying relations among elements of a social network. For example, the analysis of a set of companies working for a give"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.08040","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}