论文标题
带有泊松测量的定向优先依恋模型
A Directed Preferential Attachment Model with Poisson Measurement
论文作者
论文摘要
在建模定向的社交网络时，一种选择是使用传统的优惠依恋模型，该模型生成幂律尾巴分布。在传统的定向优先附件中，每个新的边缘都会顺序添加到网络中。但是，对于真实数据集，通常只有可用的粗图纸，这意味着在同一时间戳上创建了几个新边缘。对社交网络发展的先前分析表明，在达到稳定的阶段后，网络中边缘数量的增长遵循了一个非均匀的泊松过程，全天持续的速率，但每天都在变化。考虑到这样的经验观察，我们提出了一种使用泊松测量的改良优先依恋模型，并研究其渐近行为。然后将这种修改的模型拟合到真实数据集，我们看到它提供了比传统数据集更好的拟合度。
When modeling a directed social network, one choice is to use the traditional preferential attachment model, which generates power-law tail distributions. In a traditional directed preferential attachment, every new edge is added sequentially into the network. However, for real datasets, it is common to only have coarse timestamps available, which means several new edges are created at the same timestamp. Previous analyses on the evolution of social networks reveal that after reaching a stable phase, the growth of edge counts in a network follows a non-homogeneous Poisson process with a constant rate across the day but varying rates from day to day. Taking such empirical observations into account, we propose a modified preferential attachment model with Poisson measurement, and study its asymptotic behavior. This modified model is then fitted to real datasets, and we see it provides a better fit than the traditional one.
