【摘要】：隨著近年來計算機(jī)技術(shù)的迅猛發(fā)展,網(wǎng)絡(luò)的復(fù)雜程度以及數(shù)據(jù)量逐年增加。因此,可視化在復(fù)雜網(wǎng)絡(luò)的分析研究領(lǐng)域變得越來越重要。在許多的可視化方法中,依據(jù)社區(qū)發(fā)現(xiàn)進(jìn)行可視化已經(jīng)成為一種趨勢,并且在復(fù)雜網(wǎng)絡(luò)研究領(lǐng)域取得了良好的效果。本文對復(fù)雜網(wǎng)絡(luò)中的社區(qū)發(fā)現(xiàn)及可視化進(jìn)行了研究,主要完成的工作有三部分。第一部分是嘗試在復(fù)雜網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)的過程中通過引入模糊數(shù)學(xué)的思想,實(shí)現(xiàn)動態(tài)的網(wǎng)絡(luò)社區(qū)劃分,更好的展現(xiàn)網(wǎng)絡(luò)特性。本文選取了節(jié)點(diǎn)的度中心性和接近度中心性作為節(jié)點(diǎn)重要性測度。在具體的實(shí)現(xiàn)過程中,應(yīng)用第一次F模式識別確定社區(qū)的核心影響力節(jié)點(diǎn),第二次F模式識別確定非核心節(jié)點(diǎn)應(yīng)屬的社區(qū),兩次F模式識別就可以完成社區(qū)的劃分,識別方法都是F模式識別的直接方法,使用最大隸屬度原則。第二部分是嘗試應(yīng)用模糊數(shù)學(xué)思想,將兩種測度綜合考慮來實(shí)現(xiàn)社區(qū)發(fā)現(xiàn)。與單測度主要區(qū)別在于第二次F模式識別需要同時考慮兩個測度,因此需要采用F模式識別的間接方法,采用F集的貼近度進(jìn)行識別。最后,本文提出了基于劃分結(jié)果的可視化方案,通過Processing語言搭建了可視化平臺。實(shí)現(xiàn)了以環(huán)形布局為基礎(chǔ)的環(huán)形嵌套布局和以力引導(dǎo)模型為基礎(chǔ)的牽引聚和布局算法。嘗試實(shí)現(xiàn)社區(qū)劃分結(jié)果的動態(tài)展示,方便對網(wǎng)絡(luò)特性的研究,并最終取得了良好的效果。總而言之,本文不僅通過引入模糊數(shù)學(xué)理論成功實(shí)現(xiàn)了基于F模式識別的復(fù)雜網(wǎng)絡(luò)社區(qū)劃分,還設(shè)計并實(shí)現(xiàn)了可視化算法將劃分結(jié)果展示了出來。本文算法具備實(shí)用性的同時,也為復(fù)雜網(wǎng)絡(luò)可視化領(lǐng)域理論研究提供了一個嶄新的視角。
[Abstract]:With the rapid development of computer technology in recent years, the complexity of network and the amount of data increase year by year. Therefore, visualization is becoming more and more important in the field of complex network analysis. In many visualization methods, visualization based on community discovery has become a trend, and has achieved good results in the field of complex network research. In this paper, community discovery and visualization in complex networks are studied. The first part is to try to realize the dynamic division of network community by introducing the idea of fuzzy mathematics in the process of discovering complex network community, so as to show the characteristics of the network better. In this paper, the degree centrality and proximity centrality of nodes are selected as the measure of node importance. In the concrete realization process, the first F-pattern recognition is applied to determine the core influence node of the community, the second F-pattern recognition determines the community to which the non-core node belongs, and two F-pattern recognition can complete the division of the community. The identification method is the direct method of F pattern recognition, and the maximum membership degree principle is used. The second part is an attempt to use fuzzy mathematics to consider the two measures to realize community discovery. The main difference from single measure is that the second F pattern recognition needs to consider two measures at the same time, so it is necessary to adopt the indirect method of F pattern recognition and the close degree of F set for recognition. Finally, this paper proposes a visualization scheme based on partition results, and builds a visualization platform through Processing language. The circular nested layout based on ring layout and the traction aggregation and placement algorithm based on force-guided model are implemented. Try to realize the dynamic display of community partition results, facilitate the study of network characteristics, and finally achieve good results. In a word, this paper not only successfully realizes the complex network community partition based on F-pattern recognition by introducing fuzzy mathematics theory, but also designs and implements the visualization algorithm to show the partition results. This algorithm not only has practicability, but also provides a new perspective for the theoretical research of complex network visualization field.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：O157.5

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