本文選題：社交網(wǎng)絡(luò)　切入點(diǎn)：影響最大化　出處：《云南大學(xué)》2016年碩士論文

【摘要】：近幾年來,隨著各種社交網(wǎng)絡(luò)的迅猛發(fā)展,人與人之間的主要交流方式逐漸從線下變?yōu)榫�上,這樣,就產(chǎn)生了在社交網(wǎng)絡(luò)中如何查找最有影響力的k個(gè)用戶的問題,也就是社交網(wǎng)絡(luò)中影響最大化問題。影響最大化問題就是挖掘社交網(wǎng)絡(luò)中最有影響力的Top-k個(gè)節(jié)點(diǎn)集。之前的影響最大化問題研究中,大多只是根據(jù)網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)來查找最有影響力的用戶,而忽略了反映用戶之間親密程度的一個(gè)很重要的因素——交互行為,從而使挖掘出的最有影響力的用戶往往與實(shí)際情況有較大偏差�；诖朔N考慮,本文提出了基于交互行為的影響最大化問題,建立了一個(gè)基于用戶交互行為的影響傳播模型UIB_IC模型。在UIB_IC模型中,為了對(duì)交互行為的大小進(jìn)行定量化表示,本文提出了交互度的概念,給出了基于用戶交互行為的影響力計(jì)算方法,并進(jìn)行了歸一化處理,將之作為用戶之間的激活概率。這樣,本文就根據(jù)UIB_IC模型,提出了GAUIB算法。GAUIB算法是在貪心算法的基礎(chǔ)上改進(jìn)的,它將用戶之間的交互行為運(yùn)用到用戶之間能否激活成功的概率中,這樣就能夠更加準(zhǔn)確地衡量用戶之間的影響力大小。在GAUIB算法中,因?yàn)槠渚哂凶幽Ｐ?所以該算法可以達(dá)到63%的準(zhǔn)確性。為了提高該算法的計(jì)算效率,之后本文又對(duì)其進(jìn)行了優(yōu)化,使用CELF算法減少了計(jì)算量,使其效率有了很大提升。最后,本文通過從騰訊微博中得到的相關(guān)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)驗(yàn)證,證明GAUIB算法可以得到基于用戶交互行為的影響最大化用戶集合S。
[Abstract]:In recent years, with the rapid development of various social networks, the main way of communication between people has gradually changed from offline to online, thus the question of how to find the most influential k users in social networks has arisen.This is the problem of maximizing influence in social networks.The problem of maximizing influence is mining the most influential Top-k node set in social networks.Most of the previous studies on impact maximization only looked for the most influential users based on the topology of the network, ignoring an important factor that reflected the degree of closeness between users-interaction.As a result, the most influential users excavated often deviate from the actual situation.Based on this consideration, this paper proposes the problem of maximizing the impact based on interaction behavior, and establishes a model of impact propagation based on user interaction behavior (UIB_IC).In the UIB_IC model, in order to quantify the size of the interaction behavior, the concept of interaction degree is proposed, and the influence calculation method based on the user interaction behavior is presented, and the normalized processing is given.Use this as the activation probability between users.Therefore, according to the UIB_IC model, this paper proposes that the GAUIB algorithm. GAUIB algorithm is improved on the basis of greedy algorithm, which applies the interaction behavior between users to the probability of the success of activation between users.This makes it possible to measure the impact between users more accurately.In the GAUIB algorithm, the accuracy of the algorithm can reach 63% because of its submodule.In order to improve the computational efficiency of the algorithm, this paper then optimizes the algorithm, using the CELF algorithm to reduce the amount of calculation, so that its efficiency has been greatly improved.Finally, through the experimental verification of relevant data obtained from Tencent Weibo, it is proved that the GAUIB algorithm can obtain the maximum user set based on user interaction behavior.
【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)】：TP393.09
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本文編號(hào)：1707700