# BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERT

arXiv: https://arxiv.org/abs/2402.08236 · HTML: https://mlsanity.com/paper/2402.08236/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units](https://mlsanity.com/paper/1603.05201/) (2016)
- [Link Prediction in Social Networks: the State-of-the-Art](https://mlsanity.com/paper/1411.5118/) (2014)
- [Signed Bipartite Graph Neural Networks](https://mlsanity.com/paper/2108.09638/) (2021)
- [Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields](https://mlsanity.com/paper/1703.02819/) (2017)
- [Balance in Signed Bipartite Networks](https://mlsanity.com/paper/1909.06073/) (2019)
- [FCA2VEC: Embedding Techniques for Formal Concept Analysis](https://mlsanity.com/paper/1911.11496/) (2019)
- [HPRA: Hyperedge Prediction using Resource Allocation](https://mlsanity.com/paper/2006.11070/) (2020)
- [Hyperedge Prediction using Tensor Eigenvalue Decomposition](https://mlsanity.com/paper/2102.04986/) (2021)
- [Playing the role of weak clique property in link prediction: A friend recommendation model](https://mlsanity.com/paper/1601.05146/) (2016)

## Built on by (most cited first, 2 of 2)

- [BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder](https://mlsanity.com/paper/2503.07645/) (2025)
- [Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning](https://mlsanity.com/paper/2606.05471/) (2026)

## Abstract (arXiv)

We propose BERT4FCA, a novel method for link prediction in bipartite networks, using formal concept analysis (FCA) and BERT. Link prediction in bipartite networks is an important task that can solve various practical problems like friend recommendation in social networks and co-authorship prediction in author-paper networks. Recent research has found that in bipartite networks, maximal bi-cliques provide important information for link prediction, and they can be extracted by FCA. Some FCA-based bipartite link prediction methods have achieved good performance. However, we figured out that their performance could be further improved because these methods did not fully capture the rich information of the extracted maximal bi-cliques. To address this limitation, we propose an approach using BERT, which can learn more information from the maximal bi-cliques extracted by FCA and use them to make link prediction. We conduct experiments on three real-world bipartite networks and demonstrate that our method outperforms previous FCA-based methods, and some classic methods such as matrix-factorization and node2vec.