追踪与预测社交社区的演化
Tracking and Predicting Evolution of Social Communities
- Renssalear Polytechnic Institute（伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出算法框架研究社交网络社区演化，通过识别演化序列并关联早期结构参数，预测社区寿命，实证验证于多个大型网络。
AI中文摘要：
我们开发了一个用于研究社交网络中社区演化的算法框架。我们首先从理论基础出发，由此得出结论：一个演化过程至多与其最薄弱的环节一样强。这使我们能够提出一种高效算法，用于识别动态社交网络中的所有演化序列。我们利用该算法对多个大型社交网络中的社区演化进行了实证研究，以识别社区早期阶段中那些能够预示该社区是短暂存在还是长期存续的特征。我们的结果表明，可以将社区的寿命与其早期演化的结构参数相关联；这些结论在我们调查的所有社交网络中均具有稳健性。
英文摘要：
We develop an algorithmic framework for studying the evolution of communities in social networks. We begin with the theoretical foundation, from which we conclude that an evolution is at most as strong as its weakest link. This allows us to formulate an efficient algorithm to identify all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, to identify those features of the early stages of a community that indicate whether a community is going to be shortlived or not. Our results show that it is possible to correlate the lifespan of a community to structural parameters of its early evolution; these conclusions are robust across all the social networks we have investigated.
