本文選題：復(fù)雜網(wǎng)絡(luò) + 社團(tuán)劃分��； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：作為對現(xiàn)實(shí)生活中復(fù)雜系統(tǒng)的抽象和建模,復(fù)雜網(wǎng)絡(luò)的發(fā)展為理解現(xiàn)實(shí)生活中的復(fù)雜關(guān)系提供了很好的借鑒,通過對復(fù)雜網(wǎng)絡(luò)的研究獲得對象之間的共同特性。社團(tuán)結(jié)構(gòu)是復(fù)雜網(wǎng)絡(luò)重要的局部特征,社團(tuán)結(jié)構(gòu)是指網(wǎng)絡(luò)中節(jié)點(diǎn)之間相互連接比較緊密的子網(wǎng)絡(luò)。社團(tuán)結(jié)構(gòu)的研究可以挖掘網(wǎng)絡(luò)中潛在的小團(tuán)體,通過對社團(tuán)結(jié)構(gòu)中節(jié)點(diǎn)連邊關(guān)系的研究,從而預(yù)測網(wǎng)絡(luò)潛在的行為,因此,社團(tuán)結(jié)構(gòu)的發(fā)現(xiàn)具有重要的理論意義和實(shí)際意義。本文就復(fù)雜網(wǎng)絡(luò)中社團(tuán)發(fā)現(xiàn)問題進(jìn)行了相關(guān)研究,介紹了相關(guān)的社團(tuán)發(fā)現(xiàn)算法,在分析相關(guān)算法的基礎(chǔ)上提出了基于節(jié)點(diǎn)依賴的標(biāo)簽傳播社團(tuán)發(fā)現(xiàn)算法,本文所作的主要工作如下:(1)首先對相關(guān)的社團(tuán)發(fā)現(xiàn)算法進(jìn)行了介紹,重點(diǎn)對局部社團(tuán)發(fā)現(xiàn)算法進(jìn)行了分析,基于局部信息的標(biāo)簽傳播算法因?yàn)閯澐炙俣容^快而被廣泛應(yīng)用,但該算法同時(shí)也存在不穩(wěn)定性、劃分質(zhì)量差等問題。本文針對該算法的缺點(diǎn),根據(jù)節(jié)點(diǎn)之間的依賴關(guān)系提出了基于節(jié)點(diǎn)依賴的標(biāo)簽傳播算法,改變節(jié)點(diǎn)的初始化策略,先對網(wǎng)絡(luò)中的節(jié)點(diǎn)依據(jù)節(jié)點(diǎn)依賴度進(jìn)行聚類,得到初始社團(tuán),然后再借助于標(biāo)簽傳播算法對網(wǎng)絡(luò)中的社團(tuán)進(jìn)行調(diào)整,從而改變了原來算法中的不穩(wěn)定性,加快了算法收斂性,并在真實(shí)網(wǎng)絡(luò)上對該算法進(jìn)行了驗(yàn)證。(2)在第二部分中,基于收集到的專利合作企業(yè)之間的合作數(shù)據(jù),構(gòu)建了專利合作網(wǎng)絡(luò),并對網(wǎng)絡(luò)的相關(guān)結(jié)構(gòu)特征進(jìn)行了分析,對網(wǎng)絡(luò)的演化模型進(jìn)了推導(dǎo),并基于本文的社團(tuán)劃分算法對專利合作網(wǎng)絡(luò)進(jìn)了社團(tuán)分析,對得到的社團(tuán)結(jié)構(gòu)在現(xiàn)實(shí)中意義進(jìn)行了分析,進(jìn)一步說明了本算法的所具有的劃分的優(yōu)勢。同時(shí)也說明了本算法對于局部連接緊密的網(wǎng)絡(luò)劃分的效果較好。
[Abstract]:As the abstraction and modeling of complex systems in real life, the development of complex networks provides a good reference for understanding the complex relationships in real life, and obtains the common characteristics of objects through the study of complex networks. Community structure is an important local feature of complex network. Community structure refers to a network in which nodes are closely connected with each other. The study of community structure can excavate the potential small groups in the network, and predict the potential behavior of the network by studying the relationship between nodes and edges in the community structure. Therefore, the discovery of the community structure has important theoretical and practical significance. In this paper, the problem of community discovery in complex networks is studied, and the related community discovery algorithms are introduced. Based on the analysis of the related algorithms, a node-dependent label propagation community discovery algorithm is proposed. The main work of this paper is as follows: (1) first of all, the related community discovery algorithm is introduced, and the local community discovery algorithm is analyzed. The label propagation algorithm based on local information is widely used because of the high speed of partitioning. However, the algorithm also has some problems, such as instability, poor partition quality and so on. In view of the shortcomings of the algorithm, a label propagation algorithm based on node dependency is proposed according to the dependency relationship between nodes, which changes the initialization strategy of nodes. Firstly, the nodes in the network are clustered according to the degree of dependency, and the initial community is obtained. Then, with the help of tag propagation algorithm, the community in the network is adjusted, which changes the instability of the original algorithm, speeds up the convergence of the algorithm, and verifies the algorithm on the real network. Based on the cooperation data between patent cooperative enterprises, the patent cooperation network is constructed, and the related structure characteristics of the network are analyzed, and the evolution model of the network is deduced. Based on the community partition algorithm in this paper, the community analysis of patent cooperative network is carried out, and the significance of the community structure in reality is analyzed, which further explains the advantages of this algorithm. At the same time, it also shows that the algorithm has a good effect on the partitioning of locally connected networks.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP301.6;O157.5

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本文編號：1980031