一对一人际交流的大规模加权网络分析
Analysis of a large-scale weighted network of one-to-one human communication
摘要：
我们从手机通话记录中构建了一个由390万个节点组成的连接网络，它可以被视为社会层面上底层人类通信网络的代理。我们在每条边上分配两个权重来反映社交互动的强度，即总通话时间和18周内个人之间的累计通话次数。我们提出了一个详细的分析，这个加权网络，通过检查其程度，强度和权重分布，以及它的拓扑可拓性和加权可拓性，聚类和加权聚类，以及这些数量之间的相关性。我们给一个帐户的基序强度和相干性分布，并比较它们的随机参考系。我们还使用链接重叠的概念来衡量任何两个相邻节点的共同邻居的数量，这是一个有用的本地措施，用于识别社区的互联性。我们报告的链接的重叠和权重之间的正相关性，从而提供了强有力的定量证据弱关系假设，在社会网络分析的中心概念。网络的渗流性质取决于被移除的链接的类型和顺序，它们可以帮助理解网络的局部结构如何在全局水平上表现出来。我们希望我们的研究结果将有助于加权大规模社交网络的建模，并相信这里遵循的系统方法可以被用来研究其他加权网络。
英文摘要：
We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbors any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level. We hope that our results will contribute to modeling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks.
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