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首页> 外文期刊>Physical review letters >Stochastic Dynamical Model of a Growing Citation Network Based on a Self-Exciting Point Process
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Stochastic Dynamical Model of a Growing Citation Network Based on a Self-Exciting Point Process

机译：基于自激点过程的成长型引文网络的随机动力学模型

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摘要

We put under experimental scrutiny the preferential attachment model that is commonly accepted as a generating mechanism of the scale-free complex networks. To this end we chose a citation network of physics papers and traced the citation history of 40 195 papers published in one year. Contrary to common belief, we find that the citation dynamics of the individual papers follows the superlinear preferential attachment, with the exponent α = 1.25-1.3. Moreover, we show that the citation process cannot be described as a memoryless Markov chain since there is a substantial correlation between the present and recent citation rates of a paper. Based on our findings we construct a stochastic growth model of the citation network, perform numerical simulations based on this model and achieve an excellent agreement with the measured citation distributions.
机译：我们经过实验审查的优先附着模型，通常被认为是无标度复杂网络的生成机制。为此，我们选择了物理学论文的引文网络，并追踪了一年内发表的40 195篇论文的引文历史。与普遍的看法相反，我们发现每篇论文的引文动态遵循超线性优先依恋关系，指数为α= 1.25-1.3。此外，我们证明了引用过程不能被描述为无记忆的马尔可夫链，因为当前和近期论文的引用率之间存在很大的相关性。根据我们的发现，我们构建了引文网络的随机增长模型，并基于此模型进行了数值模拟，并与测得的引文分布实现了极好的一致性。

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