Eom Young-Ho, Jo Hang-Hyun
Laboratoire de Physique Théorique du CNRS, IRSAMC, Université de Toulouse, UPS, F-31062 Toulouse, France.
BECS, Aalto University School of Science, P.O. Box 12200, Espoo, Finland.
Sci Rep. 2014 Apr 8;4:4603. doi: 10.1038/srep04603.
The friendship paradox states that your friends have on average more friends than you have. Does the paradox "hold" for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.
友谊悖论指出，平均而言，你的朋友拥有的朋友比你拥有的朋友更多。对于收入或幸福等其他个人特征，这个悖论是否“成立”呢？为了回答这个问题，我们将友谊悖论推广到复杂网络中的任意节点特征。通过分析《物理评论》期刊的两个合作网络以及谷歌学术个人资料，我们发现广义友谊悖论（GFP）在个体和网络层面对于各种特征都成立，包括共同作者数量、被引用次数和发表文章数量。GFP的根源在于度与特征之间的正相关关系。作为GFP的一个富有成效的应用，我们提出了有效且高效的抽样方法，用于在大规模网络中识别高特征节点。我们对GFP的研究有助于理解复杂网络中网络结构与节点特征之间的相互作用。
