# Common neighbours and the local-community-paradigm for link prediction in bipartite networks

arXiv: https://arxiv.org/abs/1504.07011 · HTML: https://mlsanity.com/paper/1504.07011/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2015.

## Built on by (most cited first, 5 of 5)

- [Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected](https://mlsanity.com/paper/2501.19107/) (2025)
- [Variational Disentangled Graph Auto-Encoders for Link Prediction](https://mlsanity.com/paper/2306.11315/) (2023)
- [A Complex Network based Graph Embedding Method for Link Prediction](https://mlsanity.com/paper/2209.04884/) (2022)
- [A Review of Link Prediction Applications in Network Biology](https://mlsanity.com/paper/2312.01275/) (2023)
- [Robustness in sparse artificial neural networks trained with adaptive topology](https://mlsanity.com/paper/2602.21961/) (2026)

## Abstract (arXiv)

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.