Zhu Konglin, Li Wenzhong, Fu Xiaoming, Nagler Jan
Institute of Computer Science, Georg-August-Universität Göttingen, Göttingen, Germany.
Institute of Computer Science, Georg-August-Universität Göttingen, Göttingen, Germany; State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing, China.
PLoS One. 2014 Jun 18;9(6):e100023. doi: 10.1371/journal.pone.0100023. eCollection 2014.
Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrat's law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks.
诸如脸书、推特和 Gowalla 等在线社交网络使人们能够跨国界进行交流和互动。在过去几年里，在线社交网络对于研究个体行为、群体形成以及在线社群的出现变得越来越重要。在此，我们专注于在线社交网络平均增长特征的研究，并试图理解在看似具有长期时间相关性的集体行为背后可能存在哪些过程。与近期研究结果一致，但与吉布拉特的比例增长定律相反，我们发现平均增长率及其标准差存在标度关系。然而，人人网和推特在某些重要方面与许多社会和经济系统中的情况显著不同。尽管独立方法表明人人网和推特的时间上长期相关行为并无显著意义，但对标准差的标度分析确实表明 Gowalla 存在长期时间相关增长。然而，我们证明，在线社交网络（如 Gowalla）增长中看似长期的时间相关性，可以通过分解为具有多种进入率的时间和空间上独立的增长过程来解释。因此，我们的分析表明，时间或空间相关行为在在线社交网络的增长中并不起主要作用。
