【摘要】：真實(shí)世界的復(fù)雜系統(tǒng)通�？梢猿橄蟪晒�(jié)點(diǎn)和邊構(gòu)成的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。隨著對復(fù)雜系統(tǒng)的研究深入,復(fù)雜網(wǎng)絡(luò)分析方法存在兩方面問題。首先是復(fù)雜網(wǎng)絡(luò)模型朝著異質(zhì)化、多元化的方向發(fā)展。傳統(tǒng)的復(fù)雜網(wǎng)絡(luò)拓?fù)涫菑?fù)雜系統(tǒng)的高度抽象表達(dá)。隨著研究深入,網(wǎng)絡(luò)的關(guān)系異質(zhì)性在網(wǎng)絡(luò)研究問題越來越重要,如何對異質(zhì)復(fù)雜網(wǎng)絡(luò)進(jìn)行算法分析是一個重要的研究方向。其次是網(wǎng)絡(luò)規(guī)模越來越龐大。數(shù)據(jù)量的激增對復(fù)雜網(wǎng)絡(luò)算法的存儲和計算問題帶來了嚴(yán)峻的挑戰(zhàn),能否從大規(guī)模網(wǎng)絡(luò)拓?fù)涮崛∫粋�近似的精簡結(jié)構(gòu)是一個重要的難題。為了解決上述問題帶來的挑戰(zhàn),本文基于連邊模式對復(fù)雜網(wǎng)絡(luò)進(jìn)行研究。從現(xiàn)有的研究成果顯示,邊模式有助于研究節(jié)點(diǎn)屬性關(guān)系、網(wǎng)絡(luò)生成模型、拓?fù)浣Y(jié)構(gòu)的高階表達(dá)等的網(wǎng)絡(luò)性質(zhì)。本文利用邊模式的研究方法并結(jié)合傳統(tǒng)復(fù)雜網(wǎng)絡(luò)分析理論,研究了復(fù)雜網(wǎng)絡(luò)的關(guān)系異質(zhì)性問題和核心結(jié)構(gòu)表達(dá)問題。本文的主要貢獻(xiàn)如下:1.本文提出了一種基于多層網(wǎng)絡(luò)模型的重疊社團(tuán)發(fā)現(xiàn)算法。本文系統(tǒng)地研究了連邊社團(tuán)檢測(LCD)算法,這是一種單層網(wǎng)絡(luò)下基于連邊關(guān)系的重疊社團(tuán)挖掘算法。本文基于原始算法的缺陷提出了改進(jìn)算法,并且由于該算法在多層網(wǎng)絡(luò)模型的適用性,提出了多層網(wǎng)絡(luò)連邊社團(tuán)檢測(MLCD)算法。該算法可用于異質(zhì)關(guān)系的復(fù)雜網(wǎng)絡(luò)模型。最后利用了社團(tuán)性能檢測的LFR框架,通過MLCD與主流的Louvain和Infomap社團(tuán)發(fā)現(xiàn)算法結(jié)果進(jìn)行實(shí)驗對比,肯定了本算法的適用性和有效性。2.本文提出了一種復(fù)雜網(wǎng)絡(luò)核心影響結(jié)構(gòu)提取算法。該算法挖掘網(wǎng)絡(luò)中每個節(jié)點(diǎn)鄰域子圖內(nèi)的核心模體實(shí)例,然后將其合并構(gòu)成核心影響結(jié)構(gòu)。不同于傳統(tǒng)核心結(jié)構(gòu)挖掘方法,核心影響結(jié)構(gòu)是一個精簡的網(wǎng)絡(luò)子圖,它不僅包含了網(wǎng)絡(luò)中的核心節(jié)點(diǎn),還刻畫了核心節(jié)點(diǎn)對非核心節(jié)點(diǎn)的影響關(guān)系。同時,該結(jié)構(gòu)可以很好的體現(xiàn)原始網(wǎng)絡(luò)的拓?fù)涮卣骱统叨忍卣�。該方法適用于網(wǎng)絡(luò)參數(shù)估計、可視化分析等方面,同時也可以用于復(fù)雜系統(tǒng)的網(wǎng)絡(luò)拓?fù)涮崛栴}。綜上所述,本文以邊模式作為網(wǎng)絡(luò)的基本對象,對復(fù)雜網(wǎng)絡(luò)的關(guān)系異質(zhì)性和核心影響問題進(jìn)行了深入的研究,并且取得了有效的成果。所以,基于邊模式的復(fù)雜網(wǎng)絡(luò)分析方法可以作為未來復(fù)雜網(wǎng)絡(luò)學(xué)科發(fā)展的重要研究工具。
[Abstract]:Real world complex systems can be abstracted into a network topology composed of nodes and edges. With the development of complex system, there are two problems in complex network analysis method. The first is the development of complex network model towards heterogeneity and diversification. Traditional complex network topology is a highly abstract representation of complex systems. With the deepening of the research, the relationship heterogeneity of network is becoming more and more important in network research, and how to analyze the algorithm of heterogeneous complex network is an important research direction. Second, the scale of the network is getting larger and larger. The rapid increase of data volume brings a severe challenge to the storage and computation of complex network algorithms. It is an important problem to extract an approximate reduced structure from large-scale network topology. In order to solve the challenge brought by the above problems, this paper studies the complex network based on the connected edge mode. It is shown from the existing research results that the edge pattern is helpful to the study of the network properties of node attributes, network generation models, and the higher-order representation of topological structures. In this paper, the relationship heterogeneity problem and the core structure representation problem of complex networks are studied by using the method of edge pattern and the traditional theory of complex network analysis. The main contributions of this paper are as follows: 1. In this paper, an overlapping community discovery algorithm based on multi-layer network model is proposed. In this paper, the (LCD) algorithm for community detection with connected edges is studied systematically, which is an overlapping community mining algorithm based on the link relation in a single-layer network. This paper proposes an improved algorithm based on the defects of the original algorithm, and because of the applicability of the algorithm in the multilayer network model, a new (MLCD) algorithm for community detection in multi-layer networks is proposed. The algorithm can be applied to complex network models of heterogeneous relationships. Finally, the LFR framework of community performance detection is used, and the results of MLCD are compared with those of the popular Louvain and Infomap community discovery algorithms, and the applicability and effectiveness of this algorithm are confirmed. 2. In this paper, an algorithm for extracting the core influence structure of complex networks is proposed. In this algorithm, the core motifs in the neighborhood subgraph of each node in the network are mined, and then combined to form the core influence structure. Different from the traditional core structure mining method, the core influence structure is a concise network subgraph, which not only includes the core nodes in the network, but also describes the relationship between the core nodes and the non-core nodes. At the same time, the structure can well reflect the topology and scale characteristics of the original network. This method is suitable for network parameter estimation, visual analysis and so on. It can also be used to extract the network topology of complex systems. To sum up, this paper takes the edge pattern as the basic object of the network, studies the relationship heterogeneity and the core influence of the complex network deeply, and obtains the effective results. Therefore, the analysis method of complex network based on edge pattern can be used as an important research tool for the development of complex network in the future.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5

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本文編號：2331562