Graph distances for determining entities relationships: a topological approach to fraud detection

Given a set $\mathrm{\Omega }$ and a proximity function $\phi: \Omega \times \Omega \to \mathbb R^+$,wedefineanewmetricfor$\Omega$ by considering a path distance in $\mathrm{\Omega }$, that is considered as a complete graph. We analyze the properties of such a distance, and several procedures for defining the initial proximity matrix $(\varphi (a,b){)}_{(a,b)\in \mathrm{\Omega }\times \mathrm{\Omega }}\mathrm{.}$ 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 given public administration or other figures in which automatic fraud detection systems are needed. Using this formalism, we state our main idea regarding fraud detection, that is founded in the fact that fraud can be detected because it produces a meaningful local change of density in the metric space defined in this way.