# The Impact of Projection and Backboning on Network Topologies

## TL;DR

This paper investigates how different combinations of projection and backboning techniques influence the topology and centralization of networks derived from bipartite data, revealing significant effects on network structure.

## Contribution

It systematically analyzes the impact of various projection and backboning method combinations on bipartite network topologies, highlighting their influence on network properties.

## Key findings

- 12 method combinations form two distinct topology clusters
- Network centralization varies significantly with method choice
- Projection and backboning jointly shape network structure

## Abstract

Bipartite networks are a well known strategy to study a variety of phenomena. The commonly used method to deal with this type of network is to project the bipartite data into a unipartite weighted graph and then using a backboning technique to extract only the meaningful edges. Despite the wide availability of different methods both for projection and backboning, we believe that there has been little attention to the effect that the combination of these two processes has on the data and on the resulting network topology. In this paper we study the effect that the possible combinations of projection and backboning techniques have on a bipartite network. We show that the 12 methods group into two clusters producing unipartite networks with very different topologies. We also show that the resulting level of network centralization is highly affected by the combination of projection and backboning applied.