论文标题
网络进化模型的信息理论方法
An Information Theory Approach to Network Evolution Models
论文作者
论文摘要
通过信息理论引入和分析了一个新型的马尔可夫网络演化模型。可以证明，该模型（称为网络进化链）是一个固定和偏僻的随机过程。因此，可以将渐近均衡性能应用于它。还探索了模型的熵率和典型序列。从网络和方法中提取特定信息，以模拟连续时间域中的网络演变。此外，将ERDOS-RENYI网络演化链作为我们模型的一个子集引入，其固定分布的附加属性与ERDOS-RENYI随机图模型匹配。节点和图的固定分布与该子集的熵率一起计算。纸张末尾的仿真结果备份了证明的定理和计算值。
A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called Network Evolution Chain, is a stationary and ergodic stochastic process. Therefore, the Asymptotic Equipartition Property can be applied to it. The model's entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdos-Renyi Network Evolution Chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdos-Renyi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the paper back up the proved theorems and calculated values.
