人们如何在不断发展的在线关系网络中互动
How people interact in evolving online affiliation networks
摘要：
人类互动的研究对于理解个人，群体和社会的行为至关重要。在这里，我们观察网络的形成和演变，通过监测所有新的链接，我们定量分析的趋势，用于创建这些不断发展的在线联盟网络的关系。我们首先表明，这些概率趋势的准确估计只能通过以下网络的时间演化来实现。例如，通过网络的静态快照归因于朋友机制的普通朋友的动作被高估了两倍。对动态网络演化的详细分析表明，尽管有特征性的静态模式，但这些三角形中有一半是通过其他机制生成的。我们首先描述网络中建立联系时的每一个链接。这使我们能够描述领带形成的概率趋势，并得出如下社会学结论。添加新链接的趋势与我们预期的显著不同，如果它们不受网络中个人结构位置的影响，即，从随机链接形成。我们还发现，根据活动程度、性别、年龄、受欢迎程度和其他属性，个体之间的行为特征存在显著差异。例如，在这里分析的特定数据集中，我们发现女性的回报率是男性的三倍，而且这种差异随着年龄的增长而增加。在所有年龄段，男性比女性更倾向于与最受欢迎的人联系。另一方面，三角关系的倾向是相似的，与性别无关。我们的研究结果有助于建立现实社会网络结构的模型，并发现在不断发展的社交网络中建立联系的潜在规律。
英文摘要：
The study of human interactions is of central importance for understanding the behavior of individuals, groups and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We first show that an accurate estimation of these probabilistic tendencies can only be achieved by following the time evolution of the network. For example, actions that are attributed to the usual friend of a friend mechanism through a static snapshot of the network are overestimated by a factor of two. A detailed analysis of the dynamic network evolution shows that half of those triangles were generated through other mechanisms, in spite of the characteristic static pattern. We start by characterizing every single link when the tie was established in the network. This allows us to describe the probabilistic tendencies of tie formation and extract sociological conclusions as follows. The tendencies to add new links differ significantly from what we would expect if they were not affected by the individuals' structural position in the network, i.e., from random link formation. We also find significant differences in behavioral traits among individuals according to their degree of activity, gender, age, popularity and other attributes. For instance, in the particular datasets analyzed here, we find that women reciprocate connections three times as much as men and this difference increases with age. Men tend to connect with the most popular people more often than women across all ages. On the other hand, triangular ties tendencies are similar and independent of gender. Our findings can be useful to build models of realistic social network structures and discover the underlying laws that govern establishment of ties in evolving social networks.
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