协作网络中的社会惰性和多样性
Social inertia and diversity in collaboration networks
摘要：
随机图是研究社会互动的有用工具。特别是，加权随机图的使用允许处理关于哪些代理交互以及交互发生的程度的高水平信息。利用这一代表性，我们最近定义了一个量级，社会惯性，衡量代理人与以前的合作伙伴保持联系的渴望。为了研究这一量级，我们使用了特别适合于获得有效统计结果的协作网络，这是由于大量的可用数据库。在这项工作中，我研究了两个经验网络，IMDB电影数据库和condmat中的社会惯性。更具体地说，我专注于如何惯性关系到其他属性的图形，并显示惯性提供的信息如何相邻边的权重相关。这种影响的社会解释也提供。
英文摘要：
Random graphs are useful tools to study social interactions. In particular, the use of weighted random graphs allows to handle a high level of information concerning which agents interact and in which degree the interactions take place. Taking advantage of this representation, we recently defined a magnitude, the Social Inertia, that measures the eagerness of agents to keep ties with previous partners. To study this magnitude, we used collaboration networks that are specially appropriate to obtain valid statistical results due to the large size of publically available databases. In this work, I study the Social Inertia in two of these empirical networks, IMDB movie database and condmat. More specifically, I focus on how the Inertia relates to other properties of the graphs, and show that the Inertia provides information on how the weight of neighboring edges correlates. A social interpretation of this effect is also offered.
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