本文選題：微博 + 模塊度��； 參考：《太原理工大學(xué)》2014年碩士論文

【摘要】：微博作為一種新興的在線社會(huì)網(wǎng)絡(luò),為人們提供了一種新的社交方式和信息傳播渠道。微博以其獨(dú)特的用戶交互方式和快速的傳播速度,吸引了大量的用戶,由此演化出了一個(gè)巨大的在線社會(huì)網(wǎng)絡(luò)和信息傳播網(wǎng)絡(luò)。微博網(wǎng)絡(luò)有其獨(dú)特的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),挖掘其潛在的社區(qū)結(jié)構(gòu),發(fā)現(xiàn)信息傳播的規(guī)律,已成為當(dāng)前計(jì)算機(jī)信息等多學(xué)科的研究熱點(diǎn),受到了廣泛關(guān)注。探索和掌握微博網(wǎng)絡(luò)中社區(qū)結(jié)構(gòu)動(dòng)態(tài)演化機(jī)理和信息傳播機(jī)制,對(duì)于掌握信息傳播規(guī)律、預(yù)測(cè)傳播流行性、發(fā)現(xiàn)網(wǎng)絡(luò)群體事件、網(wǎng)絡(luò)輿情預(yù)警和控制具有重要意義。 本文以微博轉(zhuǎn)發(fā)構(gòu)成的信息傳播網(wǎng)絡(luò)為研究對(duì)象,從動(dòng)態(tài)社區(qū)結(jié)構(gòu)演化和微博流行度兩個(gè)方面進(jìn)行研究。 首先,本文改進(jìn)了基于模塊度優(yōu)化的快速社區(qū)發(fā)現(xiàn)算法。通過(guò)對(duì)原算法計(jì)算節(jié)點(diǎn)順序的比較,發(fā)現(xiàn)節(jié)點(diǎn)的掃描順序會(huì)直接影響該算法的時(shí)間效率。因此,本文引入“重疊度”的概念來(lái)刻畫(huà)鄰居節(jié)點(diǎn)間的連接強(qiáng)度；并且對(duì)算法的節(jié)點(diǎn)計(jì)算順序設(shè)計(jì)了不同的策略,通過(guò)減少節(jié)點(diǎn)移動(dòng),避免不必要的計(jì)算,提高算法的時(shí)間效率。通過(guò)在人造和真實(shí)數(shù)據(jù)集上的實(shí)驗(yàn)表明改進(jìn)的算法可以有效地提高時(shí)間效率。 其次,本文利用改進(jìn)的社區(qū)發(fā)現(xiàn)算法對(duì)微博信息傳播網(wǎng)絡(luò)的動(dòng)態(tài)社區(qū)演化進(jìn)行分析。微博信息傳播網(wǎng)絡(luò)是根據(jù)微博轉(zhuǎn)發(fā)路徑構(gòu)建的網(wǎng)絡(luò),區(qū)別于傳統(tǒng)的關(guān)系網(wǎng)絡(luò),其更能真實(shí)的反映用戶間的交互關(guān)系。本文將微博信息傳播網(wǎng)絡(luò)以一個(gè)月為間隔,按時(shí)間切成網(wǎng)絡(luò)結(jié)構(gòu)快照,分別進(jìn)行社區(qū)發(fā)現(xiàn)來(lái)分析社區(qū)的演化過(guò)程。 然后,本文對(duì)新浪微博數(shù)據(jù)集進(jìn)行了實(shí)證分析,對(duì)不同微博的流行性進(jìn)行了研究,提出“流行度”的概念。實(shí)證分析不同流行度微博的傳播速度和各周期轉(zhuǎn)發(fā)特點(diǎn),發(fā)現(xiàn)不同流行度微博的傳播機(jī)制；并且分析了早期微博轉(zhuǎn)發(fā)特征信息與微博最終轉(zhuǎn)發(fā)數(shù)存在正相關(guān)的關(guān)系,表明了通過(guò)早期的微博轉(zhuǎn)發(fā)特征信息可以有效的預(yù)測(cè)微博最終流行度。 最后,本文提出了基于支持向量機(jī)(SVM)的微博流行度預(yù)測(cè)模型,結(jié)合微博發(fā)布者的用戶特征和微博在一小時(shí)內(nèi)的轉(zhuǎn)發(fā)特征來(lái)作為模型的特征值來(lái)預(yù)測(cè)微博最終的流行度。通過(guò)實(shí)驗(yàn)對(duì)不同流行度和時(shí)間段的微博進(jìn)行預(yù)測(cè)比較,證明模型能夠更加準(zhǔn)確的預(yù)測(cè)微博流行度。
[Abstract]:As a new online social network, Weibo provides a new way of social communication and information dissemination. Weibo attracts a large number of users with its unique user interaction mode and rapid propagation speed, and thus evolves a huge online social network and information dissemination network. Weibo network has its unique network topology, mining its potential community structure, discovering the rules of information dissemination, has become the current computer information and other multidisciplinary research hot spots, has received extensive attention. It is of great significance to explore and master the dynamic evolution mechanism of community structure and the mechanism of information dissemination in Weibo network, which is of great significance for mastering the law of information dissemination, predicting the popularity of communication, discovering network group events, and early warning and control of network public opinion. In this paper, the information dissemination network based on Weibo forwarding is studied from two aspects: dynamic community structure evolution and Weibo prevalence. Firstly, this paper improves the fast community discovery algorithm based on modularity optimization. By comparing the order of nodes calculated by the original algorithm, it is found that the scanning order of the nodes will directly affect the time efficiency of the algorithm. Therefore, this paper introduces the concept of "overlap degree" to describe the connection strength between neighbor nodes, and designs different strategies for the node calculation sequence of the algorithm, which can avoid unnecessary calculation by reducing node movement. Improve the time efficiency of the algorithm. Experiments on artificial and real data sets show that the improved algorithm can effectively improve the time efficiency. Secondly, the improved community discovery algorithm is used to analyze the dynamic community evolution of Weibo information dissemination network. The Weibo information dissemination network is constructed according to the Weibo forwarding path, which is different from the traditional relational network, and it can reflect the interaction between users more truthfully. In this paper, the Weibo information dissemination network is cut into a snapshot of the network structure in a one-month interval, and the community discovery is carried out to analyze the evolution process of the community. Then, this paper makes an empirical analysis of Sina Weibo data set, studies the epidemic of different Weibo, and puts forward the concept of "popularity". This paper empirically analyzes the propagation speed and the characteristics of different cycles of Weibo, finds out the transmission mechanism of Weibo with different prevalence, and analyzes the positive correlation between the forwarding feature information of early Weibo and the final forwarding number of Weibo. The results show that early Weibo forwarding feature information can effectively predict the final popularity of Weibo. Finally, this paper presents a prediction model of Weibo popularity based on support vector machine (SVM), which combines the user characteristics of Weibo publishers and the forwarding features of Weibo within an hour as the eigenvalues of the model to predict the final popularity of Weibo. The prediction of Weibo in different prevalence and time periods by experiments shows that the model can predict Weibo prevalence more accurately.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

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本文編號(hào)：1940840