Graph Processing Frameworks
URL: https://doi.org/10.1007/978-3-319-63962-8_283-2
This chapter provides a comprehensive overview of graph processing frameworks (GPFs)—software systems designed to efficiently analyze and compute on large-scale graph data. GPFs enable the representation of entities as vertices and their relationships as edges, offering APIs and computation models that abstract low-level parallelization details while supporting diverse applications such as social network analysis, biological systems, and fraud detection. The authors classify more than twenty notable GPFs along four key dimensions: target platform (shared, distributed, or hybrid memory), computation model (MapReduce, BSP, GAS, GSA, etc.), processing approach (vertex-, edge-, subgraph-, or neighborhood-centric), and communication model (message passing, shared memory, or dataflow). Detailed discussions include classical frameworks like Pregel, GraphLab, Giraph, and GraphX, as well as recent innovations leveraging GPU, Hybrid Memory Cube (HMC), and ReRAM architectures (e.g., GraphH, GraphP, GraphR). The chapter also illustrates a practical example of computing the degree distribution of a large social network using the Apache Flink Gelly API, demonstrating scalable parallel execution. In closing, the authors identify open challenges in graph partitioning, hybrid architectures, benchmarking, and performance evaluation, emphasizing that efficient parallelization and resource-aware design remain central to advancing next-generation graph processing systems.
Información adicional
| Campo | Valor |
|---|---|
| Última actualización de los datos | 10 de octubre de 2025 |
| Última actualización de los metadatos | 10 de octubre de 2025 |
| Creado | 10 de octubre de 2025 |
| Formato | HTML |
| Licencia | No se ha provisto de una licencia |
| Id | fc171422-0921-4ca1-9239-f31b4d9ec5d7 |
| Package id | 7d26b887-9381-4f45-829b-4fb6ac60a43c |
| State | active |