A coarsening method for bipartite networks via weight-constrained label propagation
作者：
Highlights：
• A near linear matching strategy based on weight-constrained label propagation to address coarsening of bipartite networks.
• Two coarsening strategies that yield greater network reduction factors (on the order of 60%-90%) at each level.
• A comprehensive experimental evaluation of the proposed strategy on real and synthetic bipartite networks that demonstrates its scalability and solution quality.
• A discussion on the underlying applicability in the context of network visualization.
摘要
•A near linear matching strategy based on weight-constrained label propagation to address coarsening of bipartite networks.•Two coarsening strategies that yield greater network reduction factors (on the order of 60%-90%) at each level.•A comprehensive experimental evaluation of the proposed strategy on real and synthetic bipartite networks that demonstrates its scalability and solution quality.•A discussion on the underlying applicability in the context of network visualization.
论文关键词：Complex networks,Multilevel method,Large-scale networks,Bipartite networks,Community detection,Network coarsening,Network visualization
论文评审过程：Received 21 May 2019, Revised 14 February 2020, Accepted 18 February 2020, Available online 25 February 2020, Version of Record 4 April 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.105678
