标题： 动态网络的快速近似谱聚类 显示英文标题

标题： Fast Approximate Spectral Clustering for Dynamic Networks

摘要： 谱聚类是一个被广泛研究的问题，但对于中等规模的动态图来说，其复杂度仍然很高。 我们声称可以重用过去聚类分配的信息来加速计算。 我们的方法建立在最近一种避免谱聚类主要瓶颈的想法之上，即通过使用随机信号的快速切比雪夫图滤波来避免计算图特征向量。 我们证明所提出的算法能够实现与谱聚类质量相近的聚类分配，并且当图动态适当受限时，可以显著降低复杂度。 显示英文摘要

摘要： Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clustering, i.e., computing the graph eigenvectors, by using fast Chebyshev graph filtering of random signals. We show that the proposed algorithm achieves clustering assignments with quality approximating that of spectral clustering and that it can yield significant complexity benefits when the graph dynamics are appropriately bounded.