Improved Methods for k-core Community Search

Abstract

Community search based on user-specified query nodes is complementary to community finding or graph clustering. Prior work in community search is divided into optimizing for external separate- ness or internal cohesiveness, which does not scale well networks of over a billion edges. We present SteinerKCore, a new scalable k-core based community search algorithm for multi-vertex queries. We also present Par-ShellStruct, a parallel algorithm for building the ShellStruct data structure used for k-core community search. We show that our implemen- tations in Icebug, an open-source toolkit for large-scale network analysis, are both more efficient and more scalable than comparative tools, being able to perform on a benchmark network of 273M and 5.1B edges using just 64GB RAM and under 4 hours runtime with 16 CPUs.