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A Connectivity-Prior Model for Generating Connected Power Law Random Graphs with Prescribed Degree Sequence

机译：具有规定度序列的连接电源法随机图的连接前提模型

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摘要

Generating precise network topologies is an important issue for the purpose of simulating and evaluating networking applications. Recent research results reveal that the topology of Internet is neither a purely random network nor a hierarchical structure, but similar to complex networks obeying power law distributions. Under this condition, a practical degree-driven method is widely used for generating network topologies with prescribed degree sequence. To import random features, additional random transformations are required to perform upon the generated graph. In this paper, we propose a connectivity-prior algorithm to create a connected graph and develop a simple but efficient method to perform randomization operations to transform the generated graph. During the creating and transforming process, the graph is kept connected. We made experiments with the latest degree sequence data of the actually Internet topologies. The results show that our method works more efficiently.
机译：生成精确的网络拓扑是模拟和评估网络应用的重要问题。最近的研究结果表明，互联网的拓扑既不是纯粹的随机网络也不是分层结构，而是类似于遵守权力法分布的复杂网络。在这种情况下，实际的程度驱动方法广泛用于用规定度序列产生网络拓扑。要导入随机功能，请在生成的图表上执行额外的随机转换。在本文中，我们提出了一种连通性算法来创建连接的图表，并开发一个简单但有效的方法来执行随机化操作来改造所生成的图形。在创建和转换过程中，保持连接。我们用实际互联网拓扑的最新学位序列数据进行了实验。结果表明，我们的方法更有效地工作。

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