5 research outputs found
Parallel Algorithms for Generating Random Networks with Given Degree Sequences
Random networks are widely used for modeling and analyzing complex processes.
Many mathematical models have been proposed to capture diverse real-world
networks. One of the most important aspects of these models is degree
distribution. Chung--Lu (CL) model is a random network model, which can produce
networks with any given arbitrary degree distribution. The complex systems we
deal with nowadays are growing larger and more diverse than ever. Generating
random networks with any given degree distribution consisting of billions of
nodes and edges or more has become a necessity, which requires efficient and
parallel algorithms. We present an MPI-based distributed memory parallel
algorithm for generating massive random networks using CL model, which takes
time with high probability and space per processor,
where , , and are the number of nodes, edges and processors,
respectively. The time efficiency is achieved by using a novel load-balancing
algorithm. Our algorithms scale very well to a large number of processors and
can generate massive power--law networks with one billion nodes and
billion edges in one minute using processors.Comment: Accepted in NPC 201