# Bipartite graph analysis as an alternative to reveal clusterization in complex systems

arXiv: https://arxiv.org/abs/1806.04406 · HTML: https://mlsanity.com/paper/1806.04406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes](https://mlsanity.com/paper/2205.01331/) (2022)

## Abstract (arXiv)

We demonstrate how analysis of co-clustering in bipartite networks may be used as a bridge to connect, compare and complement clustering results about community structure in two different spaces: single-mode bipartite network projections. As a case study we consider scientific knowledge, which is represented as a complex bipartite network of articles and related concepts. Connecting clusters of articles and clusters of concepts via article-to-concept bipartite co-clustering, we demonstrate how concept features (e.g. subject classes) may be inferred from the article ones.