Which friends are more popular than you?: Contact strength and the friendship paradox in social networks

James P. Bagrow, C. Danforth, Lewis Mitchell
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引用次数: 12

Abstract

The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, "your friends have more friends than you do". Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: "your closest friends have slightly more friends than you do", and in certain networks even: "your best friend has no more friends than you do". We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.
哪些朋友比你更受欢迎?社交网络中的联系强度和友谊悖论
友谊悖论指出,在社交网络中,自我的程度往往低于他们的变化,或者,“你的朋友比你有更多的朋友”。大多数研究都集中在友谊悖论及其对信息传播的影响上,但将网络视为静态和未加权的。然而,人们只能将注意力预算的有限部分投入到每次社会互动中:高度个体可能没有多少时间投入到个人社会联系中,这迫使他们调整与不同社会关系的接触数量。在本研究中,我们研究了自我和他人之间不同接触量下的友谊悖论,发现接触量与友谊悖论的强度之间存在联系。与自我相比,联系最频繁的人表现出不太明显的友谊悖论,而联系较少的人则更有可能是高度的,并产生这种悖论。因此,我们认为友谊悖论有一个更微妙的版本:“你最亲密的朋友比你拥有更多的朋友”,甚至在某些网络中:“你最好的朋友没有比你拥有更多的朋友”。我们证明了这种关系是稳健的,在社交媒体和移动电话数据集中都成立。这些结果对社会网络中的信息传递和影响具有启示意义,我们使用一个简单的动态模型进行了探索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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