Engineering High-Performance Community Detection Heuristics for Massive Graphs

The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism is often necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible clustering algorithm framework with shared-memory parallelism. Within this framework we implement our parallel variations of known sequential algorithms and combine them by an ensemble approach. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. The processing rate of our fastest algorithm exceeds 10M edges/second for many large graphs, making it suitable for massive data streams. Moreover, the strongest algorithm we developed yields the best tradeoff between quality and speed for graph clusterers to date.