  Understanding the higher-order interactions within network data is a key
objective of network science. Surveys of metadata triangles (or patterned
3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In
this work, we develop TriPoll, a prototype distributed HPC system capable of
surveying triangles in massive graphs containing metadata on their edges and
vertices. We contrast our approach with much of the prior effort on triangle
analysis, which often focuses on simple triangle counting, usually in simple
graphs with no metadata. We assess the scalability of TriPoll when surveying
triangles involving metadata on real and synthetic graphs with up to hundreds
of billions of edges.We utilize communication-reducing optimizations to
demonstrate a triangle counting task on a 224 billion edge web graph in
approximately half of the time of competing approaches, while additionally
supporting metadata-aware capabilities.
