# The Impact of Projection and Backboning on Network Topologies

arXiv: https://arxiv.org/abs/1906.09081 · HTML: https://mlsanity.com/paper/1906.09081/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [Spectral Neural Graph Sparsification](https://mlsanity.com/paper/2510.27474/) (2025)
- [A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp](https://mlsanity.com/paper/2109.10462/) (2021)

## Abstract (arXiv)

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.