LOTUS: Locality Optimizing Triangle Counting

Triangle Counting (TC) is a basic graph mining problem with numerous applications. However, the large size of real-world graphs has a severe effect on TC performance. This paper studies the TC algorithm from the perspective of memory utilization. We investigate the implications of the skewed degree distribution of real-world graphs on TC and make novel observations on how memory locality is negatively affected. Based on this, we introduce the LOTUS algorithm as a structure-aware TC that optimizes locality. The evaluation on 14 real-world graphs with up to 162 billion edges and on 3 different processor architectures of up to 128 cores shows that Lotus is 2.2-5.5x faster than previous works.