本文關(guān)鍵詞： 社會(huì)網(wǎng)絡(luò) 影響力最大化 藕合相似度 主題　出處：《云南大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著網(wǎng)絡(luò)逐漸成為人與人之間主要的社交方式,在網(wǎng)絡(luò)中挖掘最有影響力的用戶成為非常值得關(guān)注的問(wèn)題。商品通過(guò)網(wǎng)絡(luò)進(jìn)行營(yíng)銷已經(jīng)成為商業(yè)戰(zhàn)場(chǎng)中主流的進(jìn)攻方式,利用好網(wǎng)絡(luò)中用戶自身的影響效應(yīng)是取得事半功倍效果的關(guān)鍵所在。如此一來(lái),社交網(wǎng)絡(luò)中的影響力最大化問(wèn)題便成為研究的焦點(diǎn)。影響力最大化問(wèn)題就是要在社交網(wǎng)絡(luò)中確定有限個(gè)種子節(jié)點(diǎn),使得這些用戶能夠在網(wǎng)絡(luò)中引起最大的影響效應(yīng)。關(guān)于該問(wèn)題的研究已經(jīng)有很多成熟的理論,比如影響力最大化問(wèn)題中經(jīng)典的貪心算法。但是傳統(tǒng)的影響力最大化問(wèn)題并沒(méi)有考慮到網(wǎng)絡(luò)中傳播的信息具有的不同主題以及這些主題之間的關(guān)系,這在一定程度上局限了影響力最大化問(wèn)題的求解精度。本文在影響力最大化問(wèn)題的基礎(chǔ)上提出了面向主題耦合的影響力最大化問(wèn)題,并且針對(duì)該問(wèn)題提出了GACT (Greedy Algorithm based on the Couped Topics)算法來(lái)挖掘在特定的傳播主題下最具有影響力的用戶。GACT算法首先分析網(wǎng)絡(luò)中不同主題之間的耦合相似性,而后使用潛在語(yǔ)義索引的方法計(jì)算用戶對(duì)于不同主題的偏好,在綜合考慮主題之間耦合相似性與用戶對(duì)不同主題偏好的基礎(chǔ)上擴(kuò)展獨(dú)立級(jí)聯(lián)模型,在擴(kuò)展的傳播模型上使用經(jīng)典的貪心算法挖掘最具有影響力的用戶,最后使用CELF算法進(jìn)行優(yōu)化以提高算法的時(shí)間效率。與經(jīng)典的影響力最大化算法相比,GACT算法能夠考慮到傳播主題之間的耦合相似性并且能夠與用戶偏好更為有效的結(jié)合,在影響力最大化問(wèn)題中挖掘出更為精確的種子節(jié)點(diǎn)。最后,在電影社交網(wǎng)絡(luò)上通過(guò)實(shí)驗(yàn)證明了GACT算法相比經(jīng)典的影響力最大化算法能夠更為有效的挖掘在特定主題下最具有影響力的用戶。
[Abstract]:As the Internet has gradually become the main form of social interaction between people. Mining the most influential users in the network has become a matter of great concern. Commodity marketing through the network has become the mainstream attack way in the business battlefield. Making good use of the influence of the user in the network is the key to achieve twice the result with half the effort. The problem of maximization of influence in social networks becomes the focus of research. The problem of maximization of influence is to determine a limited number of seed nodes in social networks. So that these users can cause the greatest impact in the network. There are many mature theories about this problem. For example, the classical greedy algorithm in the influence maximization problem, but the traditional impact maximization problem does not take into account the different topics and the relationship between the information spread in the network. To a certain extent, it limits the accuracy of solving the problem of maximization of influence. Based on the problem of maximization of influence, this paper puts forward the problem of maximization of influence oriented to subject coupling. To solve this problem, GACT greedy Algorithm based on the Couped Topics is proposed. Algorithm to mine the most influential user. Gas algorithm under a specific transmission topic. Firstly, the coupling similarity between different topics in the network is analyzed. Then the potential semantic index is used to calculate user preferences for different topics, and the independent cascading model is extended based on the consideration of the coupling similarity between topics and user preferences for different topics. In the extended propagation model, the classical greedy algorithm is used to mine the most influential users. Finally, the CELF algorithm is used to optimize to improve the time efficiency of the algorithm. GACT algorithm can take into account the coupling similarity between propagating topics, and can be combined with user preferences more effectively, mining more accurate seed nodes in the problem of maximizing the impact. Finally. The experiments on the film social network show that the GACT algorithm is more effective than the classical influence maximization algorithm in mining the most influential users under a particular topic.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP301.6;F274
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本文編號(hào)：1460144