本文選題：復(fù)雜網(wǎng)絡(luò)　切入點：切割距離　出處：《吉林大學(xué)》2017年碩士論文

【摘要】：近年來,研究各種有向復(fù)雜網(wǎng)絡(luò)之間的相似性已經(jīng)成為一個中心性的跨學(xué)科話題,并且具有大量的相關(guān)應(yīng)用領(lǐng)域,在這里相似的本質(zhì)就是相同種類網(wǎng)絡(luò)的網(wǎng)絡(luò)特征是高度相似的,不同種類的網(wǎng)絡(luò)會展現(xiàn)出很低程度的相似。在這篇文章中,我們將嘗試探索一種基于切割距離聚類各種復(fù)雜網(wǎng)絡(luò)的新方法,我們將給出一個相似網(wǎng)絡(luò)與切割距離之間的相似性,這個相似性將引導(dǎo)我們?nèi)ヌ骄扛鼮閺V泛的復(fù)雜網(wǎng)絡(luò),并且比以往的一些方法精確度會更高。在聚類過程中,我們會應(yīng)用到與機器學(xué)習(xí)技術(shù)相關(guān)的內(nèi)容,例如遺傳算法等。
[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.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;O157.5

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1 丁娜;基于切割距離的隨機圖聚類分析[D];吉林大學(xué);2017年

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本文編號：1700790