本文選題：社會(huì)媒體 + 微博��； 參考：《中國(guó)科學(xué)技術(shù)大學(xué)》2014年碩士論文

【摘要】：隨著Web2.0技術(shù)的發(fā)展,各種類型的社會(huì)媒體如雨后春筍般先后出現(xiàn),而微博,作為其中重要的一種類型,已成為人們之間相互溝通、傳播信息的重要途徑。由于微博存在一些特殊的性質(zhì),近年來(lái),針對(duì)微博的研究日益增多,其中影響力的研究成為熱點(diǎn)問(wèn)題之一,包括影響力度量和影響力最大化兩大主題。盡管已經(jīng)取得了一些成果,但是也都仍然存在一些問(wèn)題,比如在已提出的影響力最大化算法中,不是運(yùn)行時(shí)間過(guò)長(zhǎng),就是準(zhǔn)確度不高,或者是假設(shè)條件太多。在影響力度量問(wèn)題上,也仍存在一些新的角度可以去探討。因此,本文將從這兩個(gè)方面對(duì)微博中的影響力展開(kāi)研究。 本文先根據(jù)用戶交互行為的雙向性,從轉(zhuǎn)發(fā)力度、評(píng)論強(qiáng)度、提及密度和關(guān)鍵字相似度四個(gè)因素對(duì)用戶交互程度進(jìn)行評(píng)估。然后,考慮到一個(gè)用戶對(duì)其關(guān)注者的影響力貢獻(xiàn)值是變化的,且依賴于雙方的交互行為,提出一種新的影響力度量算法MBUserRank,并在此基礎(chǔ)上,對(duì)影響力最大化問(wèn)題展開(kāi)討論。接著,為了更好地近似激活概率并降低復(fù)雜度,修改MBUserRank算法并得到MBRank算法。通過(guò)MBRank對(duì)全部用戶排序,選出一部分排名靠前的用戶組成候選種子節(jié)點(diǎn)集合,在此基礎(chǔ)上引入啟發(fā),提出新的影響力最大化算法MBGreedy及其改進(jìn)算法MBCELFo最后,對(duì)生活中一個(gè)常見(jiàn)問(wèn)題——影響力排名前k的用戶是否一定會(huì)導(dǎo)致影響力最大化進(jìn)行了探索,并對(duì)候選種子集合大小的選擇進(jìn)行了說(shuō)明。 通過(guò)在騰訊微博數(shù)據(jù)集上大量實(shí)驗(yàn),得出如下結(jié)論：1)、新提出的影響力度量算法MBUserRank能夠給出更貼近現(xiàn)實(shí)的排序結(jié)果；2)、新提出的影響力最大化算法MBCELF結(jié)合了原始貪心算法和啟發(fā)式算法的優(yōu)點(diǎn),能夠在較短時(shí)間內(nèi)取得近似最優(yōu)解；3)、影響力排名前k的用戶不一定會(huì)導(dǎo)致影響力最大化。
[Abstract]:With the development of Web2.0 technology, various types of social media have appeared, and Weibo, as one of the important types, has become an important way for people to communicate and disseminate information. Due to some special properties of Weibo, the research on Weibo has been increasing in recent years, among which the research of influence has become one of the hot issues, including the measurement of influence and the maximization of influence. Although some achievements have been made, there are still some problems, such as too long running time, low accuracy, or too many assumptions in the proposed algorithm. There are still some new points of view on the measurement of influence. Therefore, this paper will study the influence of Weibo from these two aspects. Based on the bidirectional behavior of user interaction, this paper evaluates the degree of user interaction from four factors: forwarding intensity, comment intensity, reference density and keyword similarity. Then, considering that the value of a user's impact contribution to his followers is variable and dependent on the interaction between the two parties, a new influence measurement algorithm, MBUserRank, is proposed, and on this basis, the problem of maximizing the influence is discussed. Then, in order to approximate the activation probability and reduce the complexity, the MBUserRank algorithm is modified and the MBRank algorithm is obtained. All users are sorted by MBRank, and some of the top ranking users are selected to form candidate seed node sets. On the basis of this, a new influence maximization algorithm MBGreedy and its improved algorithm MBCELFo are proposed. This paper explores a common problem in life, whether the user in the top k of influence will lead to the maximization of influence, and explains the selection of the size of the candidate seed set. By experimenting extensively with Tencent Weibo datasets, The conclusions are as follows: 1: 1, the new influence measurement algorithm MBUserRank can give a more realistic ranking result, and the new influence maximization algorithm MBCELF combines the advantages of the original greedy algorithm and heuristic algorithm. It is possible to obtain approximate optimal solutions in a short period of time, and users with the top k of influence do not necessarily lead to maximum impact.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)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

【參考文獻(xiàn)】

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1 冀進(jìn)朝;韓笑;王U，

本文編號(hào)：1944035