Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, PowerGraph, GraphLab, and Trinity. However, these systems deal only with static graphs and do not consider the issue of processing evolving and dynamic graphs. In this paper, we are considering the issues of scale and dynamism in the case of graph processing systems. We present bladyg, a graph processing framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of bladyg on top of akka framework. We experimentally evaluate the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs. The experimental results show that the performance and scalability of bladyg are satisfying for large-scale dynamic graphs.

近年来，大型动态图的分布式处理已变得非常流行，尤其是在某些领域，例如社交网络分析，Web图分析和空间网络分析。在这种情况下，已经提出了许多分布式/并行图形处理系统，例如Pregel，PowerGraph，GraphLab和Trinity。但是，这些系统仅处理静态图，而不考虑处理演化图和动态图的问题。在本文中，我们正在考虑图形处理系统情况下的比例和动态性问题。我们介绍了bladyg，这是一个图形处理框架，用于解决大型图形中的动态性问题。我们在akka之上提出bladyg的实现框架。我们通过将其应用于分布式k核分解和大型动态图的划分等问题，通过实验评估了所提出框架的性能。实验结果表明，对于大型动态图，bladyg的性能和可扩展性令人满意。