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Optimal compression for bipartite networks

Author

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  • Huang, Shuhong
  • Wang, Xiangrong
  • Peng, Liyang
  • Xie, Jiarong
  • Sun, Jiachen
  • Hu, Yanqing

Abstract

Bipartite network is crucial for recommendation systems as user-product behaviors are thoroughly described 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 performance 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.

Suggested Citation

  • Huang, Shuhong & Wang, Xiangrong & Peng, Liyang & Xie, Jiarong & Sun, Jiachen & Hu, Yanqing, 2021. "Optimal compression for bipartite networks," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005610
    DOI: 10.1016/j.chaos.2021.111207
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    References listed on IDEAS

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    5. Jiarong Xie & Fanhui Meng & Jiachen Sun & Xiao Ma & Gang Yan & Yanqing Hu, 2021. "Detecting and modelling real percolation and phase transitions of information on social media," Nature Human Behaviour, Nature, vol. 5(9), pages 1161-1168, September.
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