社交网络的新模式

Sreedhar Bhukya
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引用次数: 8

摘要

近年来对社会网络的研究主要基于分类混合、高聚类、短平均路径长度、广泛度分布和社区结构的存在等特征。在此,建立了一个满足上述所有特征的模型。此外,该模型促进了不同社区之间的互动。该模型通过保持渐近无标度分布,给出了很高的聚类系数。在这里,社区结构是从随机依恋和隐性优先依恋的混合中产生的。除了早期只将初始接触邻居(NIC)视为隐式优先接触之外,我们还考虑了初始接触邻居(NNIC)。如果新顶点选择了多个初始接触点,则该模型支持在两个初始接触点之间发生接触。这最终将形成一个复杂的社会网络,而不是作为基本参考的社会网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel model for social networks
A number of recent studies on social networks are based on a characteristic which includes assortative mixing, high clustering, short average path lengths, broad degree distributions and the existence of community structure. Here, a model which satisfies all the above characteristics is developed. In addition, this model facilitates interaction between various communities. This model gives very high clustering coefficient by retaining the asymptotically scale-free degree distribution. Here the community structure is raised from a mixture of random attachment and implicit preferential attachment. In addition to earlier works which only considered Neighbour of Initial Contact (NIC) as implicit preferential contact, we have considered Neighbour of Neighbour of Initial Contact (NNIC) also. This model supports the occurrence of a contact between two Initial contacts if the new vertex chooses more than one initial contacts. This ultimately will develop a complex social network rather than the one that was taken as basic reference.
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