Many phenomena and artifacts such as road networks, social networks and the web can be modeled as large graphs and analyzed using graph algorithms. However, given the size of the underlying graphs, efficient implementation of basic operations such as connected component analysis, approximate shortest paths, and link-based ranking (e.g. PageRank) becomes challenging.
This paper presents an empirical study of computations on such large graphs in three well-studied platform models, viz., a relational model, a data-parallel model, and a special-purpose in-memory model. We choose a prototypical member of each platform model and analyze the computational efficiencies and requirements for five basic graph operations used in the analysis of real-world graphs viz., PageRank, SALSA, Strongly Connected Components (SCC), Weakly Connected Components (WCC), and Approximate Shortest Paths (ASP). Further, we characterize each platform in terms of these computations using model-specific implementations of these algorithms on a large web graph. Our experiments show that there is no single platform that performs best across different classes of operations on large graphs. While relational databases are powerful and flexible tools that support a wide variety of computations, there are computations that benefit from using special-purpose storage systems and others that can exploit data-parallel platforms.