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arXiv 1609.08381physics.soc-phcs.SI
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社会的多重建模

Multiplex Modeling of the Society

Janos Kertesz, Janos Torok, Yohsuke Murase, Hang-Hyun Jo, Kimmo Kaski

摘要:

社会是一个多层次的结构,各层次代表着不同的背景。为了对这种结构进行建模,我们首先从一个单层加权社交网络(WSN)模型开始,该模型显示了Grantwartterian结构。我们发现,当合并这样的无线传感器网络模型,需要足够的层间相关性来保持拓扑结构和链路权重之间的关系,而这些相关性破坏了社区重叠由于多个层的增强。为了解决这个问题,我们设计了一个地理多层WSN模型,其中由于个人的地理限制,间接层间的相关性增强了社区之间的重叠,同时,保留了Granteltterian结构。此外,从另一个角度来看,社会互动网络也可以被认为是一个多元化的网络:每一层都对应着一个沟通渠道,所有这些渠道的集合构成了整个社会网络。然而,通常人们只有关于其中一个通道的信息,这应该被视为整体的样本。在这里,我们通过模拟和分析方法表明,这种采样可能会导致偏差。例如,虽然预计整个社交网络的度分布在大于1的值处具有最大值,但我们对采样过程进行了合理的假设,得到了在单通道数据的实证研究中观察到的单调递减分布。我们分析了我们的研究结果的深远影响。

英文摘要:

The society has a multi-layered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of inter-layer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multi-layer WSN model, where the indirect inter-layer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved. Furthermore, the network of social interactions can be considered as a multiplex from another point of view too: each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels, which should be considered as a sample of the whole. Here we show by simulations and analytical methods that this sampling may lead to bias. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling process a monotonously decreasing distribution as observed in empirical studies of single channel data. We analyse the far-reaching consequences of our findings.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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