The benchmark harness consists of a core component, which is extendable by a driver for each different platform implementation. The benchmark includes the following algorithms:

1. breadth-first search (BFS)
2. PageRank (PR)
3. weakly connected components (WCC)
4. community detection using label propagation (CDLP)
5. local clustering coefficient (LCC)
6. single-source shortest paths (SSSP)

The choice of these algorithms was carefully motivated, using the LDBC TUC and extensive literature surveys to ensure good coverage of scenarios. The standard datasets include both real and synthetic datasets, which are classified into intuitive “T-shirt” sizes (S, M, L, etc.).

Each experiment set in Graphalytics consists of multiple platform runs (a platform executes an algorithm on a dataset), and diverse set of experiments are carried out to evaluate different performance characteristics of a system-under-test.

All completed benchmarks must go through a strict validation process to ensure the integrity of the performance results.

The development of Graphalytics is supported by many active vendors in the field of large-scale graph analytics. Currently, Graphalytics already facilitates benchmarks for a large number of graph analytics platforms, such as GraphBLAS, Giraph, GraphX, and PGX.D, allowing comparison of the state-of-the-art system performance of both community-driven and industrial-driven platforms. To get started, the details of the Graphalyics documentation and its software components are described below.