及物性和度可解释性：社交网络的二分结构
Transitivity and degree assortativity explained: The bipartite structure of social networks
摘要：
动态过程，如知识、观点、病原体、“假新闻”、创新等的传播，高度依赖于它们发生的社会网络的结构。然而，为什么大多数社交网络呈现出一些特殊的结构特征，即高水平的传递性和程度的可传递性，当与其他类型的网络相比，问题仍然是开放的。首先，我们认为，每一个单模网络可以被视为一个二分网络的投影，并表明这是使用两个简单的例子解决了生成函数形式主义的情况。其次，使用合成和经验数据，我们揭示了如何结合的程度分布的两组节点的二分网络-连同存在的周期长度为4和6-解释了观察到的水平的传递性和程度的非线性的单模式投影网络。顶部节点度比底部节点更右偏分布的二分网络会导致高度传递和度依赖性投影，特别是当二分结构中存在大量小循环时。
英文摘要：
Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news", innovation, and others, are highly dependent on the structure of the social network on which they occur. However, questions on why most social networks present some particular structural features, namely high levels of transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network --- together with the presence of cycles of length four and six --- explains the observed levels of transitivity and degree assortativity in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.
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