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Generating Graphs by Creating Associative and Random Links Between Existing Nodes
Journal of Statistical Physics ( IF 1.6 ) Pub Date : 2020-03-06 , DOI: 10.1007/s10955-020-02517-z
Muhammad Irfan Yousuf , Suhyun Kim

The study and analysis of real-world social, communication, information and citation networks for understanding their structure and identifying interesting patterns have cultivated the need for designing generative models for such networks. A generative model generates an artificial but a realistic-looking network with the same characteristics as that of a real network under study. In this paper, we propose a new generative model for generating realistic networks. Our proposed model is a blend of three key ideas namely preferential attachment, associativity of social links and randomness in real networks. We present a framework that first tests these ideas separately and then blends them into a mixed model based on the idea that a real-world graph could be formed by a mixture of these concepts. Our model can be used for generating static as well as time evolving graphs and this feature distinguishes it from previous approaches. We compare our model with previous methods for generating graphs and show that it outperforms in several aspects. We compare our graphs with real-world graphs across many metrics such as degree, clustering coefficient and path length distributions, assortativity, eigenvector centrality and modularity. In addition, we give both qualitative and quantitative results for clarity.

中文翻译:

通过在现有节点之间创建关联和随机链接来生成图

对现实世界的社会、通信、信息和引文网络的研究和分析以了解其结构和识别有趣的模式,已经培养了为此类网络设计生成模型的需求。生成模型生成一个人工但看起来逼真的网络,其特征与所研究的真实网络的特征相同。在本文中,我们提出了一种用于生成现实网络的新生成模型。我们提出的模型融合了三个关键思想,即优先依恋、社会链接的关联性和真实网络中的随机性。我们提出了一个框架,首先分别测试这些想法,然后基于这些概念的混合可以形成现实世界图的想法,将它们混合成一个混合模型。我们的模型可用于生成静态图和时间演化图,此功能将其与以前的方法区分开来。我们将我们的模型与之前生成图形的方法进行了比较,并表明它在几个方面都表现出色。我们将我们的图与真实世界的图进行了许多指标的比较,例如度、聚类系数和路径长度分布、分类性、特征向量中心性和模块化。此外,为了清楚起见,我们同时给出了定性和定量结果。特征向量中心性和模块化。此外,为了清楚起见,我们同时给出了定性和定量结果。特征向量中心性和模块化。此外,为了清楚起见,我们同时给出了定性和定量结果。
更新日期:2020-03-06
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