A framework for distributed graph computation and machine learning at wechat scale, for more details, see 柏拉图简介 | Plato Introduction.

Authors(In alphabetical order): Benli Li, Conghui He, Donghai Yu, Pin Gao, Shijie Sun, Wenqiang Wu, Wanjing Wei, Xing Huang, Xiaogang Tu, Yangzihao Wang, Yongan Li.

Contact: [email protected]

Special thanks to Xiaowei Zhu and many for their work Gemini[1]. Several basic utility functions in Plato is derived from Gemini, the design principle of some dual-mode based algorithms in Plato is also heavily influenced by Gemini's dualmode-engine. Thanks to Ke Yang and many for their work KnightKing[2] which served as foundation of plato's walk-engine.

To simplify installation, Plato currently downloads and builds most of its required dependencies by calling following commands. You should call it at least once before any build operations.

# install compile dependencies.
sudo ./docker/install-dependencies.sh
# download and build staticlly linked libraries.
./3rdtools.sh distclean && ./3rdtools.sh install

Plato was developed and tested on x86_64 cluster and Centos 7.0. Theoretically, it can be ported to other Linux distribution easily.

./build.sh

./scripts/run_pagerank_local.sh

Prerequisite:

1. A cluster which can submit MPI programs(Hydra is a feasible solution).
2. An accessible HDFS where Plato can find its input and put output on it.

A sample submit script was locate in here, modify it based on your cluster's environment and run.

./scripts/run_pagerank.sh

• 支持算法列表
• 集群资源配置建议 | Notes on Resource Assignment

[1] Xiaowei Zhu, Wenguang Chen, Weimin Zheng, Xiaosong Ma. Gemini: A computation-centric distributed graph processing system. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16)

[2] Ke Yang, Mingxing Zhang, Kang Chen, Xiaosong Ma, Yang Bai, Yong Jiang. KnightKing: A Fast Distributed Graph Random Walk Engine. In ACM SIGOPS 27th Symposium on Operating Systems Principles (SOSP ’19)

在 Plato 系统上实现 PersonalizedPageRank（个性化pagerank）、TrustRank（信任指数）、BeliefPropagation（置信度传播）三个图算法。

• 核心算法文件: plato/algo/ppr/personalized_pagerank.hpp
• 算法 CLI 应用文件: example/personalized_pagerank.cc
• 算法运行脚本: scripts/run_ppr_local.sh
• 基于 PUSH-PULL 切换优化的算法版本: example/pushpull_ppr.cc 和 scripts/run_pushpull_ppr_local.sh
• 算法正确性验证: 参照 Spark-GraphX 和 Neo4j PageRank 相关实现

• 核心算法文件: plato/algo/trustrank/trustrank.hpp
• 算法 CLI 应用文件: example/trustrank.cc
• 算法运行脚本: scripts/run_trustrank_local.sh
• 算法正确性验证: 参照 TrustRank 论文 及 bhaveshgawri/PageRank 相关实现

• 核心算法文件: plato/algo/bp/belief_propagation.hpp
• 算法 CLI 应用文件: example/belief_propagation.cc
• 算法运行脚本: scripts/run_bp_local.sh
• 算法正确性验证: 参照 HewlettPackard/sandpiper 和 mbforbes/py-factorgraph 相关实现