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.

翻译：我们提出BERT4FCA，一种结合形式概念分析（FCA）与BERT的二部图网络链接预测新方法。二部图网络链接预测是解决社交网络好友推荐、作者-论文网络合著关系预测等实际问题的关键技术。近期研究发现，二部图网络中的最大双团簇可为链接预测提供重要信息，且可通过FCA进行提取。基于FCA的二部图链接预测方法已取得良好效果，但现有方法对提取的最大双团簇信息利用不充分，性能仍有提升空间。针对这一局限，我们提出采用BERT的方法，从FCA提取的最大双团簇中学习更丰富的信息用于链接预测。在三个真实二部图网络上的实验表明，本方法不仅优于现有FCA方法，且超越了矩阵分解、node2vec等经典方法。