BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder
This work addresses scalability issues in link prediction for bipartite networks, which is important for applications like product recommendations and medical interactions, though it is incremental as it builds on existing bi-clique-based methods.
The paper tackles the problem of link prediction in bipartite networks by proposing BicliqueEncoder, which uses formal concept analysis and a Transformer encoder to improve scalability on large datasets, achieving high prediction performance with reduced computational resources as demonstrated on five large real-world datasets.
We propose a novel and efficient method for link prediction in bipartite networks, using \textit{formal concept analysis} (FCA) and the Transformer encoder. Link prediction in bipartite networks finds practical applications in various domains such as product recommendation in online sales, and prediction of chemical-disease interaction in medical science. Since for link prediction, the topological structure of a network contains valuable information, many approaches focus on extracting structural features and then utilizing them for link prediction. Bi-cliques, as a type of structural feature of bipartite graphs, can be utilized for link prediction. Although several link prediction methods utilizing bi-cliques have been proposed and perform well in rather small datasets, all of them face challenges with scalability when dealing with large datasets since they demand substantial computational resources. This limits the practical utility of these approaches in real-world applications. To overcome the limitation, we introduce a novel approach employing iceberg concept lattices and the Transformer encoder. Our method requires fewer computational resources, making it suitable for large-scale datasets while maintaining high prediction performance. We conduct experiments on five large real-world datasets that exceed the capacity of previous bi-clique-based approaches to demonstrate the efficacy of our method. Additionally, we perform supplementary experiments on five small datasets to compare with the previous bi-clique-based methods for bipartite link prediction and demonstrate that our method is more efficient than the previous ones.