【摘要】：近些年來(lái),影響力最大化問(wèn)題已經(jīng)是數(shù)據(jù)挖掘領(lǐng)域炙手可熱的研究方向,并且普遍應(yīng)用于社會(huì)網(wǎng)絡(luò)分析。然而,現(xiàn)有大部分研究在尋找最具影響力的種子節(jié)點(diǎn)的同時(shí)忽略了一個(gè)事實(shí),那就是針對(duì)不同的話題,所選出的種子節(jié)點(diǎn)也是不同的。即使少部分現(xiàn)有研究考慮了話題因素,它們忽略了任何需要在網(wǎng)絡(luò)中傳播的商品或事件都是由多個(gè)話題組成的,只考慮單個(gè)話題是片面且不準(zhǔn)確的。同時(shí),網(wǎng)絡(luò)中用戶的興趣往往也不是單一的,而正是用戶的行為偏好直接決定了影響在社會(huì)網(wǎng)絡(luò)中傳播的結(jié)果。針對(duì)現(xiàn)有傳播模型和算法在傳播過(guò)程中未考慮多話題因素的缺陷,本文首先利用話題模型對(duì)文本信息進(jìn)行建模,再將得到的話題分布結(jié)合到傳統(tǒng)的獨(dú)立級(jí)聯(lián)模型(Independent Cascade Model)中,提出了多話題敏感的獨(dú)立級(jí)聯(lián)模型(Multi-Topic Sensitive Independent Cascade model,MTSIC模型)。利用MTSIC模型,可以幫助選出最貼近現(xiàn)實(shí)生活情況的種子節(jié)點(diǎn)。同時(shí)利用Topical HITS算法,獲得用戶的權(quán)威度和從眾性,并融入到模型中,使模型更加準(zhǔn)確。由于在網(wǎng)絡(luò)上傳播的商品或事件可能存在地理位置限制,因此地理位置信息也被當(dāng)作一項(xiàng)重要因素加入到模型中。傳統(tǒng)影響力最大化算法并不適用于多話題場(chǎng)景,因此提出多話題敏感的影響力最大化算法(Activation Nodes Similarity algorithm,ANS)�？紤]到在大規(guī)模網(wǎng)絡(luò)中進(jìn)行影響力最大化分析是十分耗時(shí)的,因此本文提出了基于Spark的多話題敏感影響力最大化算法(Parallelization of Multi-Topic algorithm,PMT)并行算法以提高算法效率。由于傳統(tǒng)評(píng)價(jià)度量未能體現(xiàn)多話題因素的重要性,因此本文提出了新的度量SIS來(lái)詮釋影響力最大化算法效果。通過(guò)在數(shù)據(jù)集DBLP和Twitter上的實(shí)驗(yàn)結(jié)果顯示,MTSIC模型可以更準(zhǔn)確的模擬真實(shí)情況下節(jié)點(diǎn)的激活情況并且ANS算法可以找到在現(xiàn)實(shí)情況中更傾向于接受商品或事件并進(jìn)行傳播的種子節(jié)點(diǎn)。而PMT算法的高效性也被證明。從各個(gè)方面進(jìn)行的實(shí)驗(yàn)結(jié)果證明了本文所提出的傳播模型及算法是效且高效的。
[Abstract]:In recent years, the problem of maximization of influence has been a hot research direction in the field of data mining, and is widely used in social network analysis. However, most of the existing studies ignore the fact that the seed nodes selected are different for different topics while looking for the most influential seed nodes. Even though a small number of existing studies consider topic factors, they ignore that any commodity or event that needs to be spread in the network is composed of multiple topics, only considering that a single topic is one-sided and inaccurate. At the same time, the interest of users in the network is often not single, and it is the behavior preference of users that directly determines the results that affect the spread of social networks. Aiming at the defects of the existing propagation models and algorithms which do not consider the multi-topic factors in the propagation process, this paper first uses the topic model to model the text information, and then combines the topic distribution into the traditional Independent cascade Model. A multi-topic sensitive independent cascade model (MTSIC model) is proposed. The MTSIC model can be used to select the seed nodes closest to the real life conditions. At the same time, the Topical hits algorithm is used to obtain the authority and conformity of the user, and it is integrated into the model to make the model more accurate. Because the goods or events propagated over the network may have geographical location restrictions, geographical location information is added to the model as an important factor. The traditional influence maximization algorithm is not suitable for multi-topic scenarios, so a multi-topic sensitive influence maximization algorithm (ans) is proposed. Considering that the analysis of influence maximization in large-scale networks is time-consuming, a parallel algorithm of multi-topic sensitive influence maximization (PMT) based on Spark is proposed to improve the efficiency of the algorithm. Because the traditional evaluation measure can not reflect the importance of multi-topic factors, this paper proposes a new measure SIS to explain the effect of the influence maximization algorithm. The experimental results on DBLP and Twitter show that the MTSIC model can more accurately simulate the activation of nodes in real situations and ans algorithm can find seed nodes that are more likely to accept commodities or events and propagate in real situations. The efficiency of PMT algorithm has also been proved. The experimental results show that the proposed propagation model and algorithm are effective and efficient.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;G206

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本文編號(hào)：2137780