标题： 具有泊松增长的偏好附着模型用于无标度网络 显示英文标题

标题： A preferential attachment model with Poisson growth for scale-free networks

摘要： 我们提出了一种具有可调幂律指数的无标度网络模型。 我们称之为泊松增长模型，它是Barabási和Albert提出的著名模型的一个分支，在该模型中，网络从一个小的种子网络迭代生成；每一步都会添加一个节点以及若干条与网络中已存在节点优先连接的边。 我们模型的关键特征在于，每一步添加的边数是一个服从泊松分布的随机变量，并且，与巴拉巴西-阿尔伯特模型中固定这一数量不同，它可以生成任何网络。 我们的模型受到一种在贝叶斯推断中的应用启发，这种推断通过马尔可夫链蒙特卡洛方法来估计网络；为此，我们还给出了在我们模型下网络的概率公式。 显示英文摘要

摘要： We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barab\'{a}si and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barab\'{a}si-Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.