AI 中文总结
本研究针对分布式介数中心性计算，提出轻量级系统级全局终止检测算法，经多类网络测试，该算法可避免本地停止的过早终止问题，保障计算零误差，凸显协调停止的必要性。
AI 中文摘要
在大型网络上计算介数中心性本质上代价高昂，因为该计算需要聚合所有顶点对之间的最短路径依赖关系，且随着网络规模扩大，其可扩展性会越来越差。可扩展的分布式算法可助力此类计算，尤其在集中式处理不可行、且消息交换需严格控制的场景（如带宽受限或超大规模网络）中。然而，现有分布式介数中心性实现未集成轻量级系统级终止检测器，这会导致局部收敛后仍产生额外消息，若配置不当还会引发过早停止。本研究提出一种适用于该任务的轻量级系统级全局终止检测算法，该算法能让顶点在本地判断整个系统是否已收敛。为对比全局终止检测与顶点自身估计稳定后便单独终止的本地停止策略，研究人员实现了自定义Python模拟器，并在合成网络（Erdos-Renyi网络、几何网络）和真实网络（Email网络、Road网络）拓扑上对两种方法进行测试。结果表明，系统级终止检测可让顶点在检测到全局收敛后安全停止，在所有测试网络中最终误差为零，而本地停止方法在异构网络上会导致过早终止并产生部分误差。本研究强调分布式中心性计算中协调停止的必要性。
英文摘要
Computing betweenness centrality on large networks is inherently expensive, as it requires aggregating shortest-path dependencies across all pairs of vertices and becomes increasingly difficult to scale as network size grows. Scalable distributed algorithms can facilitate such computations, particularly when centralised processing is not feasible, and message exchanges must be carefully controlled, for example, in bandwidth-limited or very large-scale networks. However, existing distributed betweenness centrality implementations do not integrate a lightweight, system-wide termination detector. As a consequence, this can lead to extra messaging after local convergence or, if misconfigured, premature stops. In this work, a lightweight, system-wide global termination detection algorithm for this task is presented. The proposed method enables vertices to decide locally when the overall system has converged. The method is evaluated against a local stopping strategy in which vertices terminate individually once their own estimates stabilise. To compare these two approaches, namely global termination detection and local stopping, a custom Python simulator is implemented, and both approaches are tested on synthetic (Erdos-Renyi and geometric) and real (Email and Road) network topologies. Our results show that system-wide termination detection lets vertices stop safely after detecting global convergence, as indicated by zero final error in the evaluated networks, rather than stopping independently based only on local convergence. The local stopping approach, on the other hand, results in premature termination and some errors on heterogeneous networks. This work emphasises the need for coordinated halting in distributed centrality computation.
CommentsLicense updated; manuscript unchanged
