用于中心性分析的破碎和压缩网络
Shattering and Compressing Networks for Centrality Analysis
摘要：
网络中谁更重要？谁控制节点之间的流量，或者谁的贡献对连接很重要？在回答这些问题时，中心性指标发挥着重要作用。介数度量对网络分析很有用，并在各种工具中实现。由于它是图挖掘中计算量最大的内核之一，因此已经提出了几种快速计算介数中心性的技术。在这项工作中，我们提出并研究了压缩网络并将其分解成碎片的技术，以便可以独立处理每个碎片的其余计算。虽然我们设计并调整了中间性的破碎过程，但它可以直接适用于其他中心性指标。实验结果表明，所提出的技术可以是一个很大的武器库，以减少各种类型的网络的中心性计算时间。
英文摘要：
Who is more important in a network? Who controls the flow between the nodes or whose contribution is significant for connections? Centrality metrics play an important role while answering these questions. The betweenness metric is useful for network analysis and implemented in various tools. Since it is one of the most computationally expensive kernels in graph mining, several techniques have been proposed for fast computation of betweenness centrality. In this work, we propose and investigate techniques which compress a network and shatter it into pieces so that the rest of the computation can be handled independently for each piece. Although we designed and tuned the shattering process for betweenness, it can be adapted for other centrality metrics in a straightforward manner. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types of networks.
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。
