Optimal Compression For Bipartite Networks

Bipartite network is crucial for recommendation systems as user-product behaviors are thoroughly de-scribed by bipartite interactions. Almost all of the state-of-the-art network compression algorithms are designed for general networks without harnessing the unique bipartite structure. Until 2017, Basu and Varshney proposed a compression algorithm, BSZIP, selectively for bipartite networks. However, the per-formance of this algorithm is not clear. Here, we derive the structural entropy which is equivalent to the compression limit for unlabeled random bipartite networks. Theoretically, we show that BSZIP algorithm asymptotically achieves the analytical limit. (c) 2021 Elsevier Ltd. All rights reserved.