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arXiv 1804.08548cs.DCcs.DS
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异步Gossip模型中的特征向量计算和社区发现

Eigenvector Computation and Community Detection in Asynchronous Gossip Models

Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco

摘要:

我们给出了一个简单的分布式算法计算的邻接矩阵特征向量的通信图在异步八卦模型。我们展示了如何使用该算法给国家的最先进的异步社区发现算法时,通信图是从研究的随机块模型。我们的方法也适用于一个自然的随机通信的替代模型,其中一个社区内的节点比不同社区的节点更频繁地通信。我们的分析简化和推广以前的工作,锻造异步特征向量计算和Oja的算法流主成分分析之间的连接。我们希望我们的工作作为一个起点,建立进一步的连接之间的分析随机迭代法,如Oja的算法,并工作在异步和八卦型算法的分布式计算。

英文摘要:

We give a simple distributed algorithm for computing adjacency matrix eigenvectors for the communication graph in an asynchronous gossip model. We show how to use this algorithm to give state-of-the-art asynchronous community detection algorithms when the communication graph is drawn from the well-studied stochastic block model. Our methods also apply to a natural alternative model of randomized communication, where nodes within a community communicate more frequently than nodes in different communities. Our analysis simplifies and generalizes prior work by forging a connection between asynchronous eigenvector computation and Oja's algorithm for streaming principal component analysis. We hope that our work serves as a starting point for building further connections between the analysis of stochastic iterative methods, like Oja's algorithm, and work on asynchronous and gossip-type algorithms for distributed computation.

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