Linear Work Generation of R-MAT Graphs

Abstract

R-MAT is a simple, widely used recursive model for generating `complex network' graphs with a power law degree distribution and community structure. We make R-MAT even more useful by reducing the required work per edge from logarithmic to constant. The algorithm works in an embarrassingly parallel way.

References in corpus (4)

• Emergence of scaling in random networks
• Benchmark graphs for testing community detection algorithms
• Hyperbolic Geometry of Complex Networks
• Linear Work Generation of R-MAT Graphs

Cited by in corpus (2)

• Linear Work Generation of R-MAT Graphs
• Engineering a Distributed-Memory Triangle Counting Algorithm