流图挑战：随机块划分
Streaming Graph Challenge: Stochastic Block Partition
摘要：
分析真实世界图的一个重要目标是在大型流图上实现可扩展的性能。一个具有挑战性和相关的例子是图划分问题。作为一个组合问题，图划分是NP-难的，但现有的松弛方法提供了合理的近似解决方案，可以扩展到大型图。事实证明，竞争基准和挑战是推进最先进性能和促进社区协作的有效手段。本文描述了一个图分区的挑战与基线分区算法的次二次复杂性。该算法采用严格的贝叶斯推理方法的基础上的统计模型，捕捉现实世界的图形的特征。这种强大的基础使算法能够解决众所周知的图划分方法的局限性，如模块化最大化。本文介绍了这一挑战的各个方面，包括：（1）数据集和流图生成器，（2）具有伪代码的基线划分算法，（3）关于并行化贝叶斯推理的正确性的论证，（4）不同的并行计算策略，诸如基于节点的并行和基于矩阵的并行，（5）用于划分正确性和计算要求的评估度量，（6）基于Python的演示代码和开放源代码C++代码的初步定时，以及（7）以流方式划分图的考虑。该算法的数据集和源代码以及指标，以及详细的文档可在GraphChallenge. org上获得。
英文摘要：
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
