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Adaptive decentralized routing and detection of overlapping communities.

机译:自适应分散式路由和重叠社区的检测。

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This dissertation studies routing in small-world networks such as grids plus long-range edges and real networks. Kleinberg showed that geography-based greedy routing in a grid-based network takes an expected number of steps polylogarithmic in the network size, thus justifying empirical efficiency observed beginning with Milgram. A counterpart for the grid-based model is provided; it creates all edges deterministically and shows an asymptotically matching upper bound on the route length.;The main goal is to improve greedy routing through a decentralized machine learning process. Two considered methods are based on weighted majority and an algorithm of de Farias and Megiddo, both learning from feedback using ensembles of experts. Tests are run on both artificial and real networks, with decentralized spectral graph embedding supplying geometric information for real networks where it is not intrinsically available.;An important measure analyzed in this work is overpayment, the difference between the cost of the method and that of the shortest path. Adaptive routing overtakes greedy after about a hundred or fewer searches per node, consistently across different network sizes and types. Learning stabilizes, typically at overpayment of a third to a half of that by greedy. The problem is made more difficult by eliminating the knowledge of neighbors' locations or by introducing uncooperative nodes. Even under these conditions, the learned routes are usually better than the greedy routes.;The second part of the dissertation is related to the community structure of unannotated networks. A modularity-based algorithm of Newman is extended to work with overlapping communities (including considerably overlapping communities), where each node locally makes decisions to which potential communities it belongs. To measure quality of a cover of overlapping communities, a notion of a node contribution to modularity is introduced, and subsequently the notion of modularity is extended from partitions to covers.;The final part considers a problem of network anonymization, mostly by the means of edge deletion. The point of interest is utility preservation. It is shown that a concentration on the preservation of routing abilities might damage the preservation of community structure, and vice versa.
机译:本文研究的是小世界网络中的路由,例如网格,远程边缘和真实网络。克莱因伯格(Kleinberg)表明,基于网格的网络中基于地理位置的贪婪路由在网络规模上采取了预期的多对数步数,从而证明了从米尔格拉姆开始观察到的经验效率是合理的。提供了基于网格的模型的副本;它确定性地创建所有边缘,并在路由长度上显示渐近匹配的上限。主要目标是通过分散的机器学习过程来改善贪婪路由。两种考虑的方法均基于加权多数以及de Farias和Megiddo的算法,均使用专家组从反馈中学习。测试在人工和真实网络上进行,分散的频谱图嵌入可为本质上不可用的真实网络提供几何信息。;这项工作中分析的一项重要措施是多付,方法成本与方法成本之间的差异最短的路径。在每个节点上进行约一百次或更少的搜索后,在不同的网络规模和类型中,自适应路由都将超过贪婪。学习趋于稳定,通常贪婪地多付了三分之一到一半的费用。通过消除对邻居位置的了解或通过引入不合作的节点,使问题变得更加困难。即使在这样的条件下,学习的路径通常也比贪婪的路径要好。论文的第二部分与无注释网络的社区结构有关。 Newman的基于模块的算法已扩展为可与重叠社区(包括大量重叠的社区)一起工作,在该社区中,每个节点都在本地决定其所属的潜在社区。为了衡量重叠社区的覆盖质量,引入了节点对模块化的贡献的概念,随后将模块化的概念从分区扩展到覆盖。最后一部分考虑了网络匿名化的问题,主要是通过以下方式边缘删除。兴趣点是实用程序保存。结果表明,专注于路由能力的保存可能会破坏社区结构的保存,反之亦然。

著录项

  • 作者

    Bakun, Oleg.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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