标题： 基于社区检测的自组网数据驱动聚类 显示英文标题

标题： Data-driven Clustering in Ad-hoc Networks based on Community Detection

摘要： 工业网络的高需求导致了日益庞大的传感器网络。然而，网络的复杂性和对精确数据的需求要求更高的稳定性和通信质量。传统针对自组织网络的聚类方法基于拓扑和连通性，导致聚类结果不稳定且通信质量较低。本文着眼于两种情况：时变网络和多信道自组织网络。我们将自组织网络建模为图，并将社区检测方法引入这两种情况。特别是，在时变网络中，我们的方法利用社区检测的结果来确保稳定性。通过使用相似性或人机交互措施，我们构建了一个新的加权图以进行最终聚类。在多信道网络中，我们从多层社区检测的结果中进行分配。真实数据集上的实验表明，我们的方法在稳定性和质量方面都优于基准方法。 显示英文摘要

摘要： High demands for industrial networks lead to increasingly large sensor networks. However, the complexity of networks and demands for accurate data require better stability and communication quality. Conventional clustering methods for ad-hoc networks are based on topology and connectivity, leading to unstable clustering results and low communication quality. In this paper, we focus on two situations: time-evolving networks, and multi-channel ad-hoc networks. We model ad-hoc networks as graphs and introduce community detection methods to both situations. Particularly, in time-evolving networks, our method utilizes the results of community detection to ensure stability. By using similarity or human-in-the-loop measures, we construct a new weighted graph for final clustering. In multi-channel networks, we perform allocations from the results of multiplex community detection. Experiments on real-world datasets show that our method outperforms baselines in both stability and quality.