本文選題：轉(zhuǎn)發(fā)概率 + 在線社交網(wǎng)絡��； 參考：《西安電子科技大學》2014年碩士論文

【摘要】：隨著社交網(wǎng)絡平臺的迅速發(fā)展,人們在社交網(wǎng)絡上的活動越來越多,以國內(nèi)新浪微博為例,在2013年微博用戶數(shù)已達到6億。因此越來越多的研究者將在線社交網(wǎng)絡作為研究對象。在在線社交網(wǎng)絡中,用戶可以討論自己的想法,發(fā)表自己的意見,表達自己的興趣等,所有這些行為產(chǎn)生了大量的社交數(shù)據(jù)。其中,如何對信息在整個網(wǎng)絡上的傳播進行模擬成為一個熱點研究話題。轉(zhuǎn)發(fā)行為是組成信息傳播的原子行為。因此,本文先從影響轉(zhuǎn)發(fā)行為的因素出發(fā)得出轉(zhuǎn)發(fā)概率,然后提出了基于轉(zhuǎn)發(fā)行為的信息傳播模型,最后在真實的數(shù)據(jù)集中對信息傳播進行了模擬。實驗表明,提出的模型能夠克服傳統(tǒng)模型中固定轉(zhuǎn)發(fā)概率帶來的同質(zhì)性,能夠更好地模擬真實信息傳播過程。具體工作如下:微博數(shù)據(jù)的抓取。本文搭建了基于Hadoop的分布式微博數(shù)據(jù)抓取平臺。在八臺機器上,對微博的用戶數(shù)據(jù)、關系數(shù)據(jù)和文本內(nèi)容數(shù)據(jù)進行了抓取。轉(zhuǎn)發(fā)行為的分析。通過對微博數(shù)據(jù)分析來選擇合適的特征和相應的模型。利用相應的特征和模型,得到用戶之間的轉(zhuǎn)發(fā)概率,本文對每個用戶采用了邏輯回歸模型來進行數(shù)據(jù)擬合,首先對數(shù)據(jù)進行預處理,提取出相應的特征,將處理后的數(shù)據(jù)輸入邏輯回歸模型進行訓練,最后根據(jù)模型得出每個用戶的轉(zhuǎn)發(fā)概率。信息傳播模型的模擬。首先,在微博數(shù)據(jù)中抽取出網(wǎng)絡結構。由于用戶之間的轉(zhuǎn)發(fā)組成了信息的傳播,根據(jù)轉(zhuǎn)發(fā)行為的分析,本文提出了pSIS?與pIC?傳播模型。最后,在提取的網(wǎng)絡中進行了傳播模擬的實驗。通過實驗,本文發(fā)現(xiàn)了一些新的現(xiàn)象:在傳播模型中,擁有固定轉(zhuǎn)發(fā)概率的信息傳播雖然在傳播范圍上跟真實社交網(wǎng)絡中相同,但其傳播速度明顯低于真實網(wǎng)絡中的信息傳播。另外,初始信息發(fā)布人的選擇對于信息傳播也是非常重要的,經(jīng)過研究表明某種程度上這種選擇可以使我們做出有效的策略來控制謠言的傳播與輿情監(jiān)控。
[Abstract]:With the rapid development of social networking platforms, people are becoming more and more active on social networks. Taking Sina Weibo as an example, the number of subscribers reached 600 million in 2013. Therefore, more and more researchers take the online social network as the research object. In online social networks, users can discuss their ideas, express their opinions, express their interests and so on, all of which produce a lot of social data. Among them, how to simulate the spread of information on the whole network has become a hot topic. Forwarding behavior is the atomic behavior that constitutes the transmission of information. Therefore, this paper first obtains the forwarding probability from the factors that affect the forwarding behavior, then puts forward the information transmission model based on the forwarding behavior, and finally simulates the information transmission in the real data set. Experiments show that the proposed model can overcome the homogeneity brought by the fixed forwarding probability in the traditional model and can better simulate the process of real information transmission. The specific work is as follows: Weibo data capture. This paper builds a distributed Weibo data grab platform based on Hadoop. In eight machines, Weibo's user data, relational data and text content data were grabbed. Analysis of forwarding behavior Through the Weibo data analysis to select the appropriate characteristics and the corresponding model. Using the corresponding features and models, the forwarding probability between users is obtained. In this paper, the logical regression model is used to fit the data for each user. Firstly, the data is preprocessed to extract the corresponding features. The processed data input logical regression model is trained and the forwarding probability of each user is obtained according to the model. Simulation of information transmission model. First, the network structure is extracted from Weibo data. Since forwarding between users constitutes the spread of information, according to the analysis of forwarding behavior, this paper proposes a pSIS? With PICs? Propagation model. Finally, the propagation simulation experiment is carried out in the extracted network. Through experiments, we find some new phenomena: in the transmission model, the information transmission with fixed forwarding probability is the same as that in the real social network, but its propagation speed is obviously lower than that in the real network. In addition, the choice of initial information publisher is also very important for information dissemination. The research shows that to some extent, this choice can enable us to make effective strategies to control the spread of rumors and public opinion monitoring.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09

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