在图中听到聚类：一种分布式算法
Hearing the clusters in a graph: A distributed algorithm
摘要：
我们提出了一种新的分布式聚类算法。该算法恢复从谱聚类得到的解决方案，而不需要昂贵的特征值/向量计算。我们证明，通过传播波通过图形，局部快速傅立叶变换产生的拉普拉斯矩阵的每个特征向量的局部分量，从而提供聚类信息。对于大型图，所提出的算法是数量级的速度比基于随机游走的方法。我们证明了所提出的算法的等价性谱聚类和收敛速度。我们证明了使用这种分散的聚类算法在社会图中的社区发现，加速传感器网络中的分布式估计和分布式多代理搜索策略的有效计算的好处。
英文摘要：
We propose a novel distributed algorithm to cluster graphs. The algorithm recovers the solution obtained from spectral clustering without the need for expensive eigenvalue/vector computations. We prove that, by propagating waves through the graph, a local fast Fourier transform yields the local component of every eigenvector of the Laplacian matrix, thus providing clustering information. For large graphs, the proposed algorithm is orders of magnitude faster than random walk based approaches. We prove the equivalence of the proposed algorithm to spectral clustering and derive convergence rates. We demonstrate the benefit of using this decentralized clustering algorithm for community detection in social graphs, accelerating distributed estimation in sensor networks and efficient computation of distributed multi-agent search strategies.
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