基于切割距离的随机图聚类分析

发布时间：2018-04-02 14:22

本文选题：复杂网络　切入点：切割距离　出处：《吉林大学》2017年硕士论文

【摘要】：近年来,研究各种有向复杂网络之间的相似性已经成为一个中心性的跨学科话题,并且具有大量的相关应用领域,在这里相似的本质就是相同种类网络的网络特征是高度相似的,不同种类的网络会展现出很低程度的相似。在这篇文章中,我们将尝试探索一种基于切割距离聚类各种复杂网络的新方法,我们将给出一个相似网络与切割距离之间的相似性,这个相似性将引导我们去探究更为广泛的复杂网络,并且比以往的一些方法精确度会更高。在聚类过程中,我们会应用到与机器学习技术相关的内容,例如遗传算法等。
[Abstract]:In recent years, the study of similarity between various directed complex networks has become a central interdisciplinary topic, and has a large number of related applications.The essence of similarity here is that the network characteristics of the same type of network are highly similar, and the network of different types will exhibit a very low degree of similarity.In this paper, we will try to explore a new method of clustering complex networks based on cutting distance. We will give a similarity between similar networks and cutting distances.This similarity will lead us to explore a wider range of complex networks and will be more accurate than some previous methods.In the clustering process, we will apply to the content related to machine learning technology, such as genetic algorithm.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;O157.5

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