BC tree-based spectral sampling for big complex network visualization

Abstract Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining visualization. However, existing often fail preserve connectivity important structures original graph. This paper introduces a new divide conquer approach spectral based on connectivity, called BC Tree (i.e., decomposition connected into biconnected components) sparsification. Specifically, we present two methods, vertex $$BC\_SV$$ BC_SV edge $$BC\_SS$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">BC_SS by computing effective resistance values vertices edges each component. Furthermore, $$DBC\_SS$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">DBC_SS $$DBC\_GD$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">DBC_GD , connectivity-based distributed algorithms sparsification drawing respectively, aiming further improve runtime efficiency integrating computing. Experimental results demonstrate that are significantly faster than previous while preserving same quality. obtain significant improvement over sequential approaches, achieves improvements in quality metrics layouts.