本文選題：在線社交網(wǎng)絡(luò) + 信息傳播�。� 參考：《國防科學技術(shù)大學》2014年碩士論文

【摘要】：隨著QQ、微信等網(wǎng)絡(luò)社交應(yīng)用的迅速發(fā)展,在線社交網(wǎng)絡(luò)中的信息傳播技術(shù)和信息傳播效果受到了越來越多的關(guān)注。信息傳播的影響力最大化問題研究在社會網(wǎng)絡(luò)中尋找具有給定節(jié)點數(shù)目的傳播源節(jié)點集合,通過該集合中的節(jié)點能夠使信息最終傳播到網(wǎng)絡(luò)中最廣泛的人群。傳統(tǒng)的影響力最大化問題采用覆蓋人數(shù)來評價影響效果,并未考慮用戶是否為信息的傳播目標,評價結(jié)果并不準確。面向目標人群的信息傳播要求首先對網(wǎng)絡(luò)用戶的特征進行分析,本文提出了基于PageRank的用戶特征標簽重要性分析技術(shù)UWTA。該技術(shù)利用用戶之間的關(guān)系網(wǎng)絡(luò),在用戶之間建立PageRank投票模型來分析計算不同的特征標簽對于用戶的重要性。特征標簽的重要性大小是本文判斷用戶是否為傳播目標以及最終計算信息傳播效果的指標。在線社交網(wǎng)絡(luò)中已有的信息傳播模型在解決影響力最大化問題時具有很高的時間復(fù)雜度。為提高社交網(wǎng)絡(luò)中信息傳播模型的效率,本文對廣泛使用的獨立級聯(lián)模型進行了分析研究,根據(jù)獨立級聯(lián)模型中信息傳播的概率性特征提出了基于圖精簡的CGIC模型,提高了解決影響力最大化問題的效率。實際應(yīng)用中,信息傳播是有一定代價的,解決影響力最大化問題的傳統(tǒng)方法并未考慮代價因素。本文設(shè)計了一種采用CGIC模型的貪心策略BTIDM。該策略以用戶的代價預(yù)算為限制條件,以信息在目標用戶上的傳播質(zhì)量作為評價指標,采用CGIC模型尋找對目標用戶影響力最大的傳播源節(jié)點集合。本文實驗數(shù)據(jù)來自arXiv網(wǎng)站中的作者合作關(guān)系網(wǎng)并標注了作者領(lǐng)域標簽。實驗結(jié)果表明,本文提出的UWTA技術(shù)能夠準確分析不同特征標簽對用戶的權(quán)重;在模型的時間復(fù)雜度方面,本文提出的CGIC具有更好的運行效率;在影響力傳播的效果方面,本文提出的BTIDM方法能夠在更好影響目標人群的同時把代價限制在更小范圍。
[Abstract]:With the rapid development of online social applications, such as QQ and WeChat, more and more attention has been paid to the information dissemination technology and the effect of information dissemination in online social networks. The problem of maximizing the influence of Information dissemination; A set of nodes with a given number of nodes can be found in social networks, through which information can eventually be transmitted to the widest population in the network. The traditional influence maximization problem uses the number of people to evaluate the effect of impact, and does not consider whether the user is the target of information dissemination, so the evaluation result is not accurate. The information communication for the target population requires the analysis of the characteristics of the network users. This paper presents the importance analysis technology of user feature tags based on PageRank. Using the relationship network between users, this technique establishes PageRank voting model among users to analyze and calculate the importance of different feature tags for users. The importance of feature label is the index to judge whether the user is the target of propagation and to calculate the effect of information dissemination. The existing information dissemination models in online social networks have high time complexity in solving the problem of maximizing influence. In order to improve the efficiency of the information transmission model in social networks, the widely used independent cascade model is analyzed and studied in this paper. According to the probabilistic characteristics of information transmission in the independent cascade model, a graph-based simplified CGIC model is proposed. It improves the efficiency of solving the problem of maximization of influence. In practical application, information dissemination has a certain cost, and the traditional method to solve the problem of maximizing influence does not consider the cost factor. In this paper, a greedy strategy based on CGIC model is designed. The strategy takes the cost budget of the user as the limiting condition and the quality of the information spread on the target user as the evaluation index. The CGIC model is used to find the set of propagating source nodes that have the greatest influence on the target user. The experimental data in this paper come from the author's cooperation network on arXiv website and label the author's domain. The experimental results show that the proposed UWTA technology can accurately analyze the weight of different feature tags to users; in terms of the time complexity of the model, the CGIC proposed in this paper has better running efficiency; The BTIDM method proposed in this paper can not only better affect the target population, but also limit the cost to a smaller range.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09

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