【摘要】：近些年來(lái),社交網(wǎng)絡(luò)的迅速發(fā)展對(duì)人們生活產(chǎn)生越來(lái)越深遠(yuǎn)的影響,社交網(wǎng)絡(luò)因其方便、快捷、及時(shí)等特點(diǎn)吸引了大量用戶。對(duì)社交網(wǎng)絡(luò)用戶進(jìn)行深入而全面分析與挖掘,在輿情控制、信息傳播、廣告投放等領(lǐng)域有很大意義,因此成為熱門(mén)研究?jī)?nèi)容。如今的社交網(wǎng)絡(luò)呈現(xiàn)出復(fù)雜多樣、數(shù)據(jù)量大等特點(diǎn),如何基于這些特點(diǎn),準(zhǔn)確而高效地分析社交網(wǎng)絡(luò)用戶顯得十分必要,這也是本文的主要研究?jī)?nèi)容。本文首先分析了以微博為代表的社交網(wǎng)絡(luò)的特點(diǎn),并在CASINO算法的基礎(chǔ)上,考慮了微博話題下,社交網(wǎng)絡(luò)用戶的微博點(diǎn)贊數(shù)、轉(zhuǎn)發(fā)數(shù)和評(píng)論數(shù),提出了一種改進(jìn)算法——TPURANK算法,并用該算法來(lái)分析用戶的影響力指數(shù),實(shí)驗(yàn)數(shù)據(jù)表明TPURANK算法的分析結(jié)果更具科學(xué)性和合理性。其次,本文在分析TPURANK算法的基礎(chǔ)上,結(jié)合MapReduce編程模型,提出了一個(gè)TPURANK算法的并行化方案,我們?cè)贖adoop平臺(tái)上進(jìn)行了實(shí)現(xiàn),并在不同的條件下進(jìn)行測(cè)試,實(shí)驗(yàn)結(jié)果表明,該算法與原有的CASINO算法相比,具有較好的集群性能和更快的執(zhí)行速度,這有助于我們分析社交網(wǎng)絡(luò)的海量數(shù)據(jù),以適應(yīng)社交網(wǎng)絡(luò)用戶數(shù)量越來(lái)越多、規(guī)模越來(lái)越大的特點(diǎn)。再次,在前面工作的基礎(chǔ)上,我們?cè)O(shè)計(jì)并實(shí)現(xiàn)了社交網(wǎng)絡(luò)分析系統(tǒng),包括需求分析、總體設(shè)計(jì)、功能模塊設(shè)計(jì)以及具體實(shí)現(xiàn)等工作,該系統(tǒng)具有社交網(wǎng)絡(luò)用戶影響力指數(shù)排名、依從性指數(shù)排名、用戶相關(guān)信息查詢等功能。最后,我們對(duì)社交網(wǎng)絡(luò)分系統(tǒng)的計(jì)算結(jié)果進(jìn)行了深入地分析和比較,發(fā)現(xiàn)社交用戶的影響力指數(shù)與入鏈數(shù)、點(diǎn)贊數(shù)、轉(zhuǎn)發(fā)數(shù)和評(píng)論數(shù)有關(guān),這與實(shí)際情況是非常相符的,具有較高的參考價(jià)值,因此該系統(tǒng)非常適合對(duì)社交網(wǎng)絡(luò)用戶進(jìn)行分析。此外,我們還對(duì)本論文的工作進(jìn)行了全面的總結(jié)以及對(duì)未來(lái)的工作進(jìn)行了展望。社交網(wǎng)絡(luò)作為現(xiàn)代信息傳播的重要平臺(tái)之一,有著極高的研究?jī)r(jià)值和廣闊的應(yīng)用前景,深入分析社交網(wǎng)絡(luò)用戶,不僅可以幫助我們發(fā)現(xiàn)更具價(jià)值的信息,而且能推動(dòng)社交網(wǎng)絡(luò)的發(fā)展,這也是我們的研究工作的重要目標(biāo)。
[Abstract]:In recent years, the rapid development of social networks has a more and more profound impact on people's lives. Social networks have attracted a large number of users because of their convenience, rapidity, timeliness and so on. The in-depth and comprehensive analysis and mining of social network users is of great significance in the fields of public opinion control, information dissemination, advertising and so on, so it has become a hot research content. Nowadays, social networks show many characteristics, such as complex and diverse, large amount of data and so on. Based on these characteristics, it is necessary to analyze social network users accurately and efficiently, which is also the main research content of this paper. In this paper, the characteristics of social network represented by Weibo are analyzed, and on the basis of CASINO algorithm, an improved algorithm, TPURANK algorithm, is proposed, and the analysis results of TPURANK algorithm are more scientific and reasonable, considering the Weibo topic, the number of Weibo points, the number of forwarding points and the number of comments, and the improved algorithm is used to analyze the influence index of users. The experimental data show that the analysis results of Weibo algorithm are more scientific and reasonable. Secondly, based on the analysis of TPURANK algorithm and MapReduce programming model, this paper proposes a parallelization scheme of TPURANK algorithm, which is implemented on Hadoop platform and tested under different conditions. the experimental results show that the algorithm has better cluster performance and faster execution speed than the original CASINO algorithm, which is helpful for us to analyze the massive data of social network. In order to adapt to the increasing number and scale of social network users. Thirdly, on the basis of the previous work, we design and implement a social network analysis system, including requirements analysis, overall design, functional module design and specific implementation. The system has the functions of social network user influence index ranking, compliance index ranking, user related information query and so on. Finally, we analyze and compare the calculation results of social network subsystem deeply, and find that the influence index of social users is related to the number of links, likes, forwarding and comments, which is very consistent with the actual situation and has high reference value, so the system is very suitable for the analysis of social network users. In addition, we also make a comprehensive summary of the work of this paper and look forward to the future work. As one of the important platforms of modern information dissemination, social network has high research value and broad application prospect. In-depth analysis of social network users can not only help us to find more valuable information, but also promote the development of social network, which is also an important goal of our research work.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP301.6;C912.3

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