K-truss decomposition for Scale-Free Graphs at Scale in Distributed Memory

We update our prior 2017 Graph Challenge submission [11] on large scale triangle counting in distributed memory by extending it to compute the full k-truss decomposition [6] of large scale-free graphs. We build on heuristics to minimize `wedge checks', by operating on an ordered directed graph, and describe an algorithm to `unroll' triangle counts when they are scheduled for pruning by the k-truss decomposition. Our k-truss algorithm is implemented using HavoqGT, an asynchronous vertex-centric graph analytics framework for distributed memory. We present a brief experimental evaluation on two large, real-world, scale-free graphs: a 128B edge web-graph and a 1.4B edge twitter follower graph. To our knowledge, the 128B edge web-graph is the largest real-world graph to have its k-truss decomposition computed.