This chapter provides a comprehensive overview of Graph Processing Frameworks (GPFs), which are software systems designed to efficiently process large-scale graphs across distributed and parallel computing environments. It begins by defining GPFs as toolsets for representing data as vertices and edges, emphasizing their role in managing the three challenges of big data—volume, velocity, and variety. The chapter classifies GPFs along four major dimensions: target platform, computation model, graph processing approach, and communication model. It reviews key frameworks—such as Surfer, Pegasus, GBase, Pregel, GraphX, Giraph, Gelly, GraphLab, GraphChi, and Totem—covering their underlying architectures, data abstractions, and parallelization strategies, including MapReduce, Bulk Synchronous Parallel (BSP), and Gather-Apply-Scatter (GAS) models. A practical example using Apache Flink’s Gelly demonstrates how GPF APIs can be applied to compute the degree distribution of a large social network, highlighting performance scalability with multicore systems. The chapter concludes by discussing future research directions, including graph partitioning in heterogeneous (CPU–GPU) environments, the need for standardized benchmarks, and challenges in performance evaluation and algorithmic efficiency. Overall, it situates GPFs as essential infrastructures for large-scale graph analytics in domains such as social networks, bioinformatics, and cybersecurity.
