Mathematical Modeling of Complex Networks by Using a Learning Approach

本研究的目的是将互联网作为一个复杂的网络来分析，寻找一个增长网络的数学模型，并利用这个数学模型来解释它的行为。首先提出了一种从社会网络数据中提取社区的方法。其次，在定义了社会网络中信息扩散的影响最大化问题之后，我们提出了一个非常快速的方法来解决这个问题，很好地利用键渗流。最后，我们提出了一种方法，估计可能的链路生成网络增长。针对真实的大规模网络，对所提出的方法进行了评价。

The aim of this research is to analysis the Internet which keeps growing rapidly as a complex network, to find a mathematical model for the growing network, and to explain its behavior by using the mathematical model. Firstly we proposed a method which extracts communities from social network data. Secondly, after defining an influence maximization problem for information diffusion in a social network, we proposed a very fast method to solve the problem by making good use of bond-percolation. Finally, we proposed a method which estimates probable link generations for network growth. All the proposed methods were evaluated for real large-scale networks.