MESH：一个灵活的分布式超图处理系统
MESH: A Flexible Distributed Hypergraph Processing System
摘要：
随着大型在线社交网络的快速增长，分析大规模社会结构和行为的能力变得至关重要，这导致了一些可扩展的图处理系统的开发。然而，在现实中，社交互动不仅发生在图模型中的成对个体之间，而且发生在多用户组的上下文中。研究表明，这种群体动态可以通过更一般的超图模型更好地建模，从而需要构建可扩展的超图处理系统。在本文中，我们提出了MESH，一个灵活的分布式框架可扩展超图处理。MESH提供了一个易于使用和表达的应用程序编程接口，自然扩展了许多流行的图形处理系统中常见的顶点模型。我们的框架提供了一个灵活的实现基础上的底层图处理系统，并使不同的设计选择的关键实现问题的分区超图表示。我们在Apache Spark中流行的GraphX图形处理框架之上实现了MESH。使用各种真实的数据集和在本地8节点集群以及65节点Amazon AWS测试平台上进行的实验，我们证明了MESH提供了基于数据和应用程序特征的灵活性，以及集群大小的可扩展性。我们进一步表明，它在性能上与基于Spark的另一个超图处理系统HyperX具有竞争力，同时提供了一个更简单的实现（需要少5倍的代码行），从而表明简单性和灵活性不需要以性能为代价。
英文摘要：
With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, social interaction takes place not only between pairs of individuals as in the graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through a more general hypergraph model, resulting in the need to build scalable hypergraph processing systems. In this paper, we present MESH, a flexible distributed framework for scalable hypergraph processing. MESH provides an easy-to-use and expressive application programming interface that naturally extends the think like a vertex model common to many popular graph processing systems. Our framework provides a flexible implementation based on an underlying graph processing system, and enables different design choices for the key implementation issues of partitioning a hypergraph representation. We implement MESH on top of the popular GraphX graph processing framework in Apache Spark. Using a variety of real datasets and experiments conducted on a local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate that MESH provides flexibility based on data and application characteristics, as well as scalability with cluster size. We further show that it is competitive in performance to HyperX, another hypergraph processing system based on Spark, while providing a much simpler implementation (requiring about 5X fewer lines of code), thus showing that simplicity and flexibility need not come at the cost of performance.
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