本文選題：復(fù)雜網(wǎng)絡(luò) + 壓縮�。� 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：復(fù)雜網(wǎng)絡(luò)是具有自組織、自相似、吸引子、小世界、無標(biāo)度部分或全部性質(zhì)的網(wǎng)絡(luò)�，F(xiàn)實(shí)網(wǎng)絡(luò)如社交網(wǎng)絡(luò)、交通網(wǎng)絡(luò)等都具有復(fù)雜網(wǎng)絡(luò)特性。復(fù)雜網(wǎng)絡(luò)的可視化是一個(gè)寬泛的概念,基于合理布局的可視化技術(shù)為其基本定義,從廣義上講,還可以包括基于壓縮的網(wǎng)絡(luò)保真分析和基于聚類的結(jié)構(gòu)化分析。論文研究工作即為廣義可視化技術(shù)的研究。論文的主要研究目的是為網(wǎng)絡(luò)分析決策者從整體上更好地把握網(wǎng)絡(luò)的主要成員、結(jié)構(gòu)層次關(guān)系。論文對(duì)復(fù)雜網(wǎng)絡(luò)壓縮的研究目的主要是為了更清晰地展示網(wǎng)絡(luò)的主要節(jié)點(diǎn)及主要關(guān)系,降低大規(guī)模網(wǎng)絡(luò)分析的復(fù)雜性。論文后續(xù)的社區(qū)挖掘算法及可視化布局算法以壓縮算法結(jié)果為基礎(chǔ)。壓縮方法基于圖論分析。按照網(wǎng)絡(luò)動(dòng)力學(xué)原理,節(jié)點(diǎn)是網(wǎng)絡(luò)局部的主要成因,邊是網(wǎng)絡(luò)全局的主要成因。論文分別從節(jié)點(diǎn)和邊兩方面對(duì)網(wǎng)絡(luò)進(jìn)行壓縮。節(jié)點(diǎn)的重要性以節(jié)點(diǎn)的度和聚集系數(shù)為建模指標(biāo),因?yàn)楣?jié)點(diǎn)的度反映了節(jié)點(diǎn)自身的局部聚集能力,而節(jié)點(diǎn)的聚集系數(shù)反映了節(jié)點(diǎn)對(duì)鄰居節(jié)點(diǎn)的局部聚集能力的影響;邊的重要性以邊的介數(shù)為評(píng)價(jià)指標(biāo),因?yàn)樵撝笜?biāo)反映了邊連接網(wǎng)絡(luò)不同部分的能力。論文分別用仿真數(shù)據(jù)和真實(shí)數(shù)據(jù)對(duì)所提出的壓縮算法進(jìn)行了實(shí)驗(yàn)驗(yàn)證,結(jié)果表明在壓縮比高達(dá)30-50%時(shí),壓縮后的網(wǎng)絡(luò)仍能保持60-80%的原始信息量,并仍較好地展現(xiàn)原始網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)。該算法在實(shí)際應(yīng)用時(shí)可根據(jù)原始網(wǎng)絡(luò)規(guī)模、密集度及使用者需求選擇合適的壓縮比。論文基于復(fù)雜網(wǎng)絡(luò)具有的社區(qū)特性,提出了一種基于核心節(jié)點(diǎn)的社區(qū)挖掘聚類算法。該算法以壓縮算法分析獲得的重要性較高的節(jié)點(diǎn)為初始種子節(jié)點(diǎn),保證了種子節(jié)點(diǎn)較好的局部聚集性,有益于提高聚類效率與效果。論文對(duì)采用核心節(jié)點(diǎn)可能帶來的社區(qū)重疊挖掘問題也給出了相應(yīng)的解決方案,一是依據(jù)節(jié)點(diǎn)間距離大小對(duì)核心節(jié)點(diǎn)進(jìn)行篩選,二是對(duì)社區(qū)劃分結(jié)果進(jìn)行去重疊處理。論文對(duì)聚類過程的優(yōu)化,體現(xiàn)在適應(yīng)度函數(shù)的計(jì)算綜合考慮了社區(qū)聚集度和社區(qū)自身密度兩個(gè)因素。論文給出了聚類分析的主要設(shè)計(jì),包括核心節(jié)點(diǎn)選取、適應(yīng)度函數(shù)計(jì)算、重疊節(jié)點(diǎn)處理等。實(shí)驗(yàn)結(jié)果表明:算法相比傳統(tǒng)算法聚類質(zhì)量提高。為了得到清晰直觀的復(fù)雜網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),論文提出了一種基于社區(qū)結(jié)構(gòu)的可視化布局算法。該算法在力導(dǎo)引布局算法和分層技術(shù)的基礎(chǔ)上,利用聚類得到的社區(qū)結(jié)構(gòu),自頂向下逐級(jí)展開。基于社區(qū)緊密度的KK算法用于社區(qū)間的宏觀布局,基于圓形顯示方式的FR算法用于社區(qū)內(nèi)部節(jié)點(diǎn)的微觀布局。實(shí)驗(yàn)結(jié)果表明:改進(jìn)的可視化布局美觀、時(shí)間效率也較好。此外,該算法還可以用于輔助評(píng)價(jià)社區(qū)聚類結(jié)果的好壞。因計(jì)算量的限制,論文的實(shí)驗(yàn)結(jié)果基于有限的網(wǎng)絡(luò)規(guī)模,但復(fù)雜網(wǎng)絡(luò)的特性并不局限在網(wǎng)絡(luò)規(guī)模上,論文的研究工作對(duì)大規(guī)模網(wǎng)絡(luò)仍有意義。
[Abstract]:Complex networks are networks with self-organizing, self-similar, attractor, small world, scale-free partial or total properties. Real networks, such as social networks and transportation networks, all have complex network characteristics. Visualization of complex networks is a broad concept, which is defined by visualization technology based on reasonable layout. In a broad sense, it can also include network fidelity analysis based on compression and structured analysis based on clustering. The research work of this paper is the research of generalized visualization technology. The main purpose of this paper is to better grasp the relationship between the main members of the network and the hierarchical structure for the network analysis decision makers as a whole. The purpose of this paper is to show the main nodes and their relationships more clearly, and to reduce the complexity of large-scale network analysis. The following community mining algorithms and visual layout algorithms are based on the results of the compression algorithm. The compression method is based on graph theory analysis. According to the principle of network dynamics, the node is the main cause of the local network, and the edge is the main cause of the overall situation of the network. The paper compresses the network from node and edge respectively. The importance of nodes is based on the degree and aggregation coefficient of nodes, because the degree of nodes reflects the local aggregation ability of nodes, and the clustering coefficient of nodes reflects the influence of nodes on the local aggregation ability of neighbor nodes. The importance of edge is evaluated by the index of edge because it reflects the ability of connecting different parts of network. In this paper, simulation data and real data are used to verify the proposed compression algorithm. The results show that when the compression ratio is as high as 30-50%, the compressed network can still maintain 60-80% of the original information. The topology of the original network is still well demonstrated. The algorithm can select the appropriate compression ratio according to the original network size, intensity and user demand. Based on the community characteristics of complex networks, a community mining clustering algorithm based on core nodes is proposed in this paper. In this algorithm, the most important node obtained by compression algorithm is the initial seed node, which ensures the local aggregation of the seed node, and is beneficial to improve the clustering efficiency and effect. The paper also gives the corresponding solutions to the problem of community overlap mining which may be caused by adopting core nodes. One is to screen the core nodes according to the distance between nodes, the other is to deoverlap the results of community division. In this paper, the optimization of clustering process is reflected in the calculation of fitness function which considers two factors: community aggregation and community density. This paper presents the main design of clustering analysis, including the selection of core nodes, the calculation of fitness function, the processing of overlapping nodes, and so on. The experimental results show that the clustering quality of the algorithm is better than that of the traditional algorithm. In order to obtain a clear and intuitionistic complex network topology, a visual layout algorithm based on community structure is proposed in this paper. Based on the force-guided layout algorithm and stratification technology, the algorithm is developed from top to bottom by using the community structure obtained by clustering. The KK algorithm based on community compactness is used for macro layout of community, and FR algorithm based on circular display is applied to the micro-layout of community internal nodes. The experimental results show that the improved visual layout is beautiful and the time efficiency is better. In addition, the algorithm can be used to evaluate the community clustering results. Due to the limitation of computation, the experimental results of this paper are based on the limited network size, but the characteristics of complex networks are not limited to the network scale. The research work in this paper is still meaningful for large-scale networks.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5

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本文編號(hào)：1941102