Quantifying social group evolution
Quantifying social group evolution
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DOI：
10.1038/nature05670
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发表时间：
2007-04-05
期刊：
影响因子：
64.8
通讯作者：
Vicsek, Tamas
中科院分区：
文献类型：
--
作者：
Palla, Gergely;Barabasi, Albert-Laszlo;Vicsek, Tamas
社会中个人之间的丰富互动集(1-7)导致了复杂的社区结构，捕获了社交网络中高度联系的朋友、家庭或专业集团(3，7-10)。由于个人活动和通信模式的频繁变化，相关的社会和通信网络不断演变(7，11-16)。我们对管理基本社区动态的机制的了解有限，但对于更深入地了解整个社会的发展和自我优化是必不可少的(17-22)。我们开发了一种基于团渗漏(23，24)的算法，它允许我们在大范围内调查重叠社区的时间依赖性，从而揭示表征社区演化的基本关系。我们的重点是捕捉科学家之间的合作和移动电话用户之间的通话的网络。我们发现，如果大型群体能够动态地改变他们的成员资格，那么他们持续的时间就会更长，这表明改变群体构成的能力会带来更好的适应性。小群体的行为表现出相反的趋势--稳定的条件是他们的组成保持不变。我们还表明，成员对给定社区的时间承诺的知识可以用于估计社区的寿命。这些发现提供了对小团体和大机构动态之间的根本差异的洞察。
The rich set of interactions between individuals in society(1-7) results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network(3,7-10). Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution(7,11-16). Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole(17-22). We have developed an algorithm based on clique percolation(23,24) that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing 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 for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency - the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.