基于復雜網(wǎng)絡分析的人物關系挖掘
發(fā)布時間：2018-11-14 15:26
【摘要】：真實世界的復雜系統(tǒng)通�？梢猿橄蟪晒�(jié)點和邊構成的網(wǎng)絡拓撲結構。隨著對復雜系統(tǒng)的研究深入,復雜網(wǎng)絡分析方法存在兩方面問題。首先是復雜網(wǎng)絡模型朝著異質(zhì)化、多元化的方向發(fā)展。傳統(tǒng)的復雜網(wǎng)絡拓撲是復雜系統(tǒng)的高度抽象表達。隨著研究深入,網(wǎng)絡的關系異質(zhì)性在網(wǎng)絡研究問題越來越重要,如何對異質(zhì)復雜網(wǎng)絡進行算法分析是一個重要的研究方向。其次是網(wǎng)絡規(guī)模越來越龐大。數(shù)據(jù)量的激增對復雜網(wǎng)絡算法的存儲和計算問題帶來了嚴峻的挑戰(zhàn),能否從大規(guī)模網(wǎng)絡拓撲提取一個近似的精簡結構是一個重要的難題。為了解決上述問題帶來的挑戰(zhàn),本文基于連邊模式對復雜網(wǎng)絡進行研究。從現(xiàn)有的研究成果顯示,邊模式有助于研究節(jié)點屬性關系、網(wǎng)絡生成模型、拓撲結構的高階表達等的網(wǎng)絡性質(zhì)。本文利用邊模式的研究方法并結合傳統(tǒng)復雜網(wǎng)絡分析理論,研究了復雜網(wǎng)絡的關系異質(zhì)性問題和核心結構表達問題。本文的主要貢獻如下:1.本文提出了一種基于多層網(wǎng)絡模型的重疊社團發(fā)現(xiàn)算法。本文系統(tǒng)地研究了連邊社團檢測(LCD)算法,這是一種單層網(wǎng)絡下基于連邊關系的重疊社團挖掘算法。本文基于原始算法的缺陷提出了改進算法,并且由于該算法在多層網(wǎng)絡模型的適用性,提出了多層網(wǎng)絡連邊社團檢測(MLCD)算法。該算法可用于異質(zhì)關系的復雜網(wǎng)絡模型。最后利用了社團性能檢測的LFR框架,通過MLCD與主流的Louvain和Infomap社團發(fā)現(xiàn)算法結果進行實驗對比,肯定了本算法的適用性和有效性。2.本文提出了一種復雜網(wǎng)絡核心影響結構提取算法。該算法挖掘網(wǎng)絡中每個節(jié)點鄰域子圖內(nèi)的核心模體實例,然后將其合并構成核心影響結構。不同于傳統(tǒng)核心結構挖掘方法,核心影響結構是一個精簡的網(wǎng)絡子圖,它不僅包含了網(wǎng)絡中的核心節(jié)點,還刻畫了核心節(jié)點對非核心節(jié)點的影響關系。同時,該結構可以很好的體現(xiàn)原始網(wǎng)絡的拓撲特征和尺度特征。該方法適用于網(wǎng)絡參數(shù)估計、可視化分析等方面,同時也可以用于復雜系統(tǒng)的網(wǎng)絡拓撲提取問題。綜上所述,本文以邊模式作為網(wǎng)絡的基本對象,對復雜網(wǎng)絡的關系異質(zhì)性和核心影響問題進行了深入的研究,并且取得了有效的成果。所以,基于邊模式的復雜網(wǎng)絡分析方法可以作為未來復雜網(wǎng)絡學科發(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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O157.5
本文編號：2331562
[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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O157.5
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相關碩士學位論文 前1條
1 任成磊;社會網(wǎng)絡的鄰域重疊社團劃分[D];華東師范大學;2016年
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