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Large-scale graph analysis is a critical task for big data applications. The distributed graph computing system is a successful paradigm for the large-scale graph analysis. It not only helps analysts achieve high scalability and efficiency, but also enables analysts to focus on the logic of analysis tasks through transparenting the tedious distributed communication protocols. In this book, we chose Pregel-like systems as a basic platform, and studied the deficiency of existing systems.
- 1.Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings, 2017.Google Scholar
- 3.Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D. Owens. Gunrock: A high-performance graph processing library on the gpu. In Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’16, pages 11:1–11:12, New York, NY, USA, 2016. ACM.Google Scholar
- 5.Shijie Zhou, Rajgopal Kannan, Hanqing Zeng, and Viktor K. Prasanna. An FPGA framework for edge-centric graph processing. In Proceedings of the 15th ACM International Conference on Computing Frontiers, CF ’18, pages 69–77, New York, NY, USA, 2018. ACM.Google Scholar