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

## TL;DR

This paper introduces a topological approach using graph distances to detect entities' relationships, aiming to identify fraud through local density changes in a metric space.

## Contribution

It proposes a new metric based on path distances in a complete graph to analyze relationships and detect anomalies in social network-like data.

## Key findings

- The new metric effectively captures relational structures.
- Local density changes indicate potential fraudulent activity.
- Method applicable to social network analysis and fraud detection.

## Abstract

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 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.