...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Information Theory >Structural Information and Dynamical Complexity of Networks
【24h】

Structural Information and Dynamical Complexity of Networks

机译：网络的结构信息与动态复杂性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In 1953, Shannon proposed the question of quantification of structural information to analyze communication systems. The question has become one of the longest great challenges in information science and computer science. Here, we propose the first metric for structural information. Given a graph , we define the -dimensional structural information of (or structure entropy of ), denoted by , to be the minimum overall number of bits required to determine the -dimensional code of the node that is accessible from random walk in . The -dimensional structural information provides the principle for completely detecting the natural or true structure, which consists of the rules, regulations, and orders of the graphs, for fully distinguishing the order from disorder in structured noisy data, and for analyzing communication systems, solving the Shannon’s problem and opening up new directions. The -dimensional structural information is also the first metric of dynamical complexity of networks, measuring the complexity of interactions, communications, operations, and even evolution of networks. The metric satisfies a number of fundamental properties, including additivity, locality, robustness, local and incremental computability, and so on. We establish the fundamental theorems of- the one- and two-dimensional structural information of networks, including both lower and upper bounds of the metrics of classic data structures, general graphs, the networks of models, and the networks of natural evolution. We propose algorithms to approximate the -dimensional structural information of graphs by finding the -dimensional structure of the graphs that minimizes the -dimensional structure entropy. We find that the -dimensional structure entropy minimization is the principle for detecting the natural or true structures in real-world networks. Consequently, our structural information provides the foundation for knowledge discovering from noisy data. We establish a black hole principle by using the two-dimensional structure information of graphs. We propose the natural rank of locally listing algorithms by the structure entropy minimization principle, providing the basis for a next-generation search engine.
机译：1953年，香农提出了结构信息量化的问题，以分析通信系统。这个问题已成为信息科学和计算机科学中最长的挑战之一。在这里，我们提出了结构信息的第一个指标。给定一个图，我们将的维结构信息（或的结构熵）定义为，它是确定从随机步入可访问的节点的维代码所需的最小总位数。三维结构信息提供了完全检测自然或真实结构的原理，该原理由图形的规则，规则和顺序组成，可以完全区分结构化噪声数据中的无序顺序，还可以分析通信系统，香农问题并开拓新方向。三维结构信息也是网络动态复杂性的第一个度量标准，用于衡量交互，通信，操作甚至网络演进的复杂性。度量标准满足许多基本属性，包括可加性，局部性，鲁棒性，局部和增量可计算性等。我们建立了网络的一维和二维结构信息的基本定理，包括经典数据结构，通用图，模型网络和自然演化网络的度量的上下限。我们提出了一种算法，该算法通过找到使-维结构熵最小的图-维结构来近似图的-维结构信息。我们发现-维结构熵最小化是检测现实世界网络中自然或真实结构的原理。因此，我们的结构信息为从嘈杂数据中发现知识提供了基础。我们利用图的二维结构信息建立黑洞原理。我们通过结构熵最小化原理提出了本地列表算法的自然等级，为下一代搜索引擎提供了基础。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号