Another option is to use the Pregel API, which allows users to express graph algorithms in a vertex-centric way. Pregel is designed specifically for iterative graph processing and can handle large-scale graphs by partitioning them across a Spark cluster.

One approach is to use GraphX, a Spark library for processing graph data that provides a collection of optimized graph algorithms. It leverages the power of Spark's distributed computing capabilities to efficiently process large-scale graphs.

You can also explore using GraphFrames, a Spark package for graph processing using DataFrame and SQL-like operations. It provides a high-level API for graph computations and integrates well with other Spark components such as MLlib and GraphX.