  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.
