标题： 基于自激点过程的生长网络随机动力学模型 显示英文标题

标题： Stochastic dynamical model of a growing network based on self-exciting point process

摘要： 我们对广泛接受的作为无标度复杂网络生成机制的优先连接模型进行了实验验证。 为此，我们选择了物理学论文的引用网络，并追踪了在一年内发表的40,195篇论文的引用历史。 与普遍观点相反，我们发现单个论文的引用动态遵循\emph{超线性}优先连接，其指数为$\alpha= 1.25-1.3$。 此外，我们表明，由于论文当前和近期引用率之间存在显著相关性，因此无法将引用过程描述为无记忆马尔可夫链。 基于我们的发现，我们构建了一个引用网络的随机增长模型，基于该模型进行了数值模拟，并与测量得到的引用分布达到了很好的一致。 显示英文摘要

摘要： We perform experimental verification of the preferential attachment model that is commonly accepted as a generating mechanism of the scale-free complex networks. To this end we chose citation network of Physics papers and traced citation history of 40,195 papers published in one year. Contrary to common belief, we found that citation dynamics of the individual papers follows the \emph{superlinear} preferential attachment, with the exponent $\alpha= 1.25-1.3$. Moreover, we showed that the citation process cannot be described as a memoryless Markov chain since there is substantial correlation between the present and recent citation rates of a paper. Basing on our findings we constructed a stochastic growth model of the citation network, performed numerical simulations based on this model and achieved an excellent agreement with the measured citation distributions.