量化社会群体演化
Quantifying social group evolution
摘要：
社会中个体之间丰富的互动导致了复杂的社区结构，在社交网络中捕获了高度联系的朋友圈，家庭或专业派系。由于个人的活动和通信模式经常变化，相关的社交和通信网络也在不断演变。我们的知识的机制管理的基本社区动态是有限的，但对于更深入地了解发展和自我优化的社会作为一个整体是必不可少的。我们已经开发出一种新的算法的基础上集团渗透，允许，第一次，调查的时间依赖性重叠社区在大规模上，因此，揭示基本的关系特征社区的演变。我们的重点是网络捕捉科学家之间的合作和手机用户之间的通话。我们发现，大型团体持续更长的时间，如果他们能够动态地改变他们的成员资格，这表明能够改变的组成结果在更好的适应性。小群体的行为表现出相反的趋势，稳定的条件是其组成保持不变。我们还表明，知识的时间承诺的成员到一个给定的社区可以用于估计社区的寿命。这些发现提供了一个新的观点之间的动态小团体和大机构的根本区别。
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.
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