社交網(wǎng)絡(luò)海量數(shù)據(jù)的分析與可視化
發(fā)布時間:2019-02-25 18:22
【摘要】:在社交網(wǎng)絡(luò)中,每個用戶都是一個數(shù)據(jù)源,其數(shù)據(jù)量成指數(shù)級爆炸性增長,而其中蘊(yùn)含的價值是不言而喻的。在如今的信息社會中,能否更多的掌握信息是搶占市場的關(guān)鍵。對社交網(wǎng)絡(luò)的海量數(shù)據(jù)進(jìn)行分析與可視化,正是對這些數(shù)據(jù)提取和運(yùn)用的重要過程。 數(shù)據(jù)可視化技術(shù)一直以來是學(xué)術(shù)界研究的熱點(diǎn),它可以將抽象的數(shù)據(jù)變成人們便于理解和觀察的圖形,能夠直觀的表達(dá)出數(shù)據(jù)中的信息和意義。近年來,當(dāng)數(shù)據(jù)可視化遇到大數(shù)據(jù)時,是數(shù)據(jù)可視化領(lǐng)域的又一次挑戰(zhàn)。本文針對這一問題提出了一種基于社團(tuán)發(fā)現(xiàn)的多層級可視化布局算法,通過測試和對比,驗(yàn)證其有效的降低了計算復(fù)雜度,增強(qiáng)了對海量數(shù)據(jù)展現(xiàn)的容納能力。 基于上述提出的算法,本文還實(shí)現(xiàn)了一個社交網(wǎng)絡(luò)信息可視化系統(tǒng)。該系統(tǒng)集成了數(shù)據(jù)爬取模塊,數(shù)據(jù)格式化模塊,社團(tuán)發(fā)現(xiàn)模塊,文本信息向量化模塊以及可視化模塊,可以展現(xiàn)出海量社交網(wǎng)絡(luò)的網(wǎng)絡(luò)結(jié)構(gòu)以及信息分布,并具有較強(qiáng)的數(shù)據(jù)承載能力。本文首先介紹了系統(tǒng)的需求分析與總體設(shè)計,然后介紹了系統(tǒng)各個模塊的具體實(shí)現(xiàn),最后對系統(tǒng)做了完整的測評和分析,并提出下一步的工作展望。
[Abstract]:In social networks, each user is a data source, and the amount of data increases exponentially, and its value is self-evident. In today's information society, whether to grasp more information is the key to preemptive market. Analyzing and visualizing the massive data of social network is the important process of extracting and applying these data. The technology of data visualization has always been a hot topic in academia. It can transform abstract data into graphics that people can easily understand and observe, and can express the information and meaning of data intuitively. In recent years, when data visualization meets big data, it is another challenge in the field of data visualization. In this paper, a multi-level visual layout algorithm based on community discovery is proposed. Through testing and comparison, it is proved that the algorithm effectively reduces the computational complexity and enhances the capacity of mass data display. Based on the proposed algorithm, this paper also implements a social network information visualization system. The system integrates data crawling module, data formatting module, community discovery module, text information vectorization module and visualization module, which can show the network structure and information distribution of mass social network. And has a strong data bearing capacity. This paper first introduces the requirements analysis and overall design of the system, then introduces the concrete implementation of each module of the system. Finally, it makes a complete evaluation and analysis of the system, and puts forward the future work prospects.
【學(xué)位授予單位】:北京郵電大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2014
【分類號】:TP391.41;TP393.092
本文編號:2430414
[Abstract]:In social networks, each user is a data source, and the amount of data increases exponentially, and its value is self-evident. In today's information society, whether to grasp more information is the key to preemptive market. Analyzing and visualizing the massive data of social network is the important process of extracting and applying these data. The technology of data visualization has always been a hot topic in academia. It can transform abstract data into graphics that people can easily understand and observe, and can express the information and meaning of data intuitively. In recent years, when data visualization meets big data, it is another challenge in the field of data visualization. In this paper, a multi-level visual layout algorithm based on community discovery is proposed. Through testing and comparison, it is proved that the algorithm effectively reduces the computational complexity and enhances the capacity of mass data display. Based on the proposed algorithm, this paper also implements a social network information visualization system. The system integrates data crawling module, data formatting module, community discovery module, text information vectorization module and visualization module, which can show the network structure and information distribution of mass social network. And has a strong data bearing capacity. This paper first introduces the requirements analysis and overall design of the system, then introduces the concrete implementation of each module of the system. Finally, it makes a complete evaluation and analysis of the system, and puts forward the future work prospects.
【學(xué)位授予單位】:北京郵電大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2014
【分類號】:TP391.41;TP393.092
【參考文獻(xiàn)】
相關(guān)期刊論文 前7條
1 樊鵬翼;王暉;姜志宏;李沛;;微博網(wǎng)絡(luò)測量研究[J];計算機(jī)研究與發(fā)展;2012年04期
2 李克潮;梁正友;;適應(yīng)用戶興趣變化的指數(shù)遺忘協(xié)同過濾算法[J];計算機(jī)工程與應(yīng)用;2011年13期
3 王柏;吳巍;徐超群;吳斌;;復(fù)雜網(wǎng)絡(luò)可視化研究綜述[J];計算機(jī)科學(xué);2007年04期
4 孫揚(yáng);蔣遠(yuǎn)翔;趙翔;肖衛(wèi)東;;網(wǎng)絡(luò)可視化研究綜述[J];計算機(jī)科學(xué);2010年02期
5 臺德藝;王俊;;文本分類特征權(quán)重改進(jìn)算法[J];計算機(jī)工程;2010年09期
6 吳鵬;李思昆;;適于社會網(wǎng)絡(luò)結(jié)構(gòu)分析與可視化的布局算法[J];軟件學(xué)報;2011年10期
7 肖有誥;屠成宇;;基于啟發(fā)式函數(shù)的分布式FN算法[J];計算機(jī)系統(tǒng)應(yīng)用;2012年10期
相關(guān)博士學(xué)位論文 前1條
1 孫揚(yáng);多變元網(wǎng)絡(luò)數(shù)據(jù)可視化方法研究[D];國防科學(xué)技術(shù)大學(xué);2010年
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