Random networks grown by fusing edges via urns

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
K. R. Bhutani, Ravi Kalpathy, H. Mahmoud
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引用次数: 4

Abstract

Abstract Many classic networks grow by hooking small components via vertices. We introduce a class of networks that grows by fusing the edges of a small graph to an edge chosen uniformly at random from the network. For this random edge-hooking network, we study the local degree profile, that is, the evolution of the average degree of a vertex over time. For a special subclass, we further determine the exact distribution and an asymptotic gamma-type distribution. We also study the “core,” which consists of the well-anchored edges that experience fusing. A central limit theorem emerges for the size of the core. At the end, we look at an alternative model of randomness attained by preferential hooking, favoring edges that experience more fusing. Under preferential hooking, the core still follows a Gaussian law but with different parameters. Throughout, Pólya urns are systematically used as a method of proof.
随机网络通过urn融合边缘
许多经典网络是通过将小的组件通过顶点连接起来而增长的。我们引入了一类网络,它通过将一个小图的边融合到从网络中随机均匀选择的边来生长。对于这种随机钩边网络,我们研究了其局部度分布,即顶点的平均度随时间的演化。对于一个特殊的子类,我们进一步确定了精确分布和渐近γ型分布。我们还研究了“核心”,它由经历融合的锚定良好的边缘组成。核心大小的中心极限定理出现了。最后,我们看一看通过优先挂钩获得的另一种随机性模型,倾向于经历更多融合的边。在优先挂钩下,核心仍然遵循高斯定律,但参数有所不同。自始至终,Pólya骨灰盒被系统地用作一种证明方法。
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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