混合型依恋网络的形成机制与度分布估计
Estimating Formation Mechanisms and Degree Distributions in Mixed Attachment Networks
摘要：
我们的工作介绍了一种方法来估计的贡献，连接机制的形成不断增长的网络。我们提出了一个通用的模型，其中的增长是由新的节点根据随机和优惠的连接与固定的概率连续连接驱动。过去的方法应用似然分析来估计每个机制在特定网络实例处发生的概率，利用每个时间点处的似然函数的概率。然而，连接到现有节点的概率以及因此似然函数本身随着网络的增长而变化。我们建立的条件下，应用似然分析保证存在的局部极大值的时变似然函数，并证明了期望最大化算法提供了一个收敛的估计。此外，在增长的网络中的节点的入度分布的解析特征。仿真结果表明，在所提出的条件下，期望最大化和最大似然法能够准确地估计出各机制的实际贡献，且入度分布收敛于平稳分布.
英文摘要：
Our work introduces an approach for estimating the contribution of attachment mechanisms to the formation of growing networks. We present a generic model in which growth is driven by the continuous attachment of new nodes according to random and preferential linkage with a fixed probability. Past approaches apply likelihood analysis to estimate the probability of occurrence of each mechanism at a particular network instance, exploiting the concavity of the likelihood function at each point in time. However, the probability of connecting to existing nodes, and consequently the likelihood function itself, varies as networks grow. We establish conditions under which applying likelihood analysis guarantees the existence of a local maximum of the time-varying likelihood function and prove that an expectation maximization algorithm provides a convergent estimate. Furthermore, the in-degree distributions of the nodes in the growing networks are analytically characterized. Simulations show that, under the proposed conditions, expectation maximization and maximum-likelihood accurately estimate the actual contribution of each mechanism, and in-degree distributions converge to stationary distributions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
