发帖
雷达卡
摘要翻译：
社会中个人之间的丰富互动导致了复杂的社区结构，在一个社会网络中捕捉到高度连接的朋友圈、家庭圈或专业团体。由于个体活动和交流模式的频繁变化，与之相关的社会和交流网络也在不断进化。我们对管理潜在社区动态的机制的知识有限，但对更深入地了解整个社会的发展和自我优化至关重要。我们提出了一种基于团渗流的新算法，它首次在大范围内研究了重叠群落的时间依赖性，从而揭示了表征群落演化的基本关系。我们的重点是捕捉科学家之间合作和移动电话用户之间通话的网络。我们发现，如果大群体能够动态地改变其成员组成，那么它们会持续更长的时间，这表明改变组成的能力会导致更好的适应性。小群体的行为表现出相反的趋势，稳定的条件是它们的组成保持不变。我们还证明了成员对给定社区的时间承诺的知识可以用来估计社区的寿命。这些发现提供了一个关于小团体和大机构动态之间根本差异的新观点。
---
英文标题：
《Quantifying social group evolution》
---
作者：
Gergely Palla, Albert-Laszlo Barabasi and Tamas Vicsek
---
最新提交年份：
2007
---
分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--
一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
--
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--
---
英文摘要：
The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimisation of the society as a whole. We have developed a new algorithm based on clique percolation, that allows, for the first time, to investigate the time dependence of overlapping communities on a large scale and as such, to uncover basic relationships characterising community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist longer if they are capable of dynamically altering their membership, suggesting that an ability to change the composition results in better adaptability. The behaviour of small groups displays the opposite tendency, the condition for stability being that their composition remains unchanged. We also show that the knowledge of the time commitment of the members to a given community can be used for estimating the community's lifetime. These findings offer a new view on the fundamental differences between the dynamics of small groups and large institutions.
---
PDF链接：
https://arxiv.org/pdf/704.0744
京公网安备 11010802022788号
