TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata

TL;DR

TriPoll is a distributed HPC system designed to efficiently survey metadata-enriched triangles in massive-scale graphs, significantly improving scalability and performance over prior methods, and enabling analysis of complex higher-order network interactions.

Contribution

This work introduces TriPoll, a scalable distributed system for metadata-aware triangle surveys in massive graphs, addressing limitations of prior simple, non-metadata triangle counting methods.

Findings

TriPoll efficiently surveys metadata triangles in graphs with hundreds of billions of edges.

It reduces communication overhead, halving the time compared to existing approaches.

Supports metadata-aware analysis in large-scale network data.

Abstract

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.