本文選題：社交網(wǎng)絡(luò) + 賬戶檢測(cè)�。� 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：社交網(wǎng)絡(luò)是web2.0時(shí)代興起的一種網(wǎng)絡(luò)服務(wù)，它將線下的社交活動(dòng)拓展到線上，允許用戶注冊(cè)賬戶并在網(wǎng)絡(luò)上進(jìn)行交互。社交網(wǎng)絡(luò)提倡良好的線上社交行為，但是依舊存在通過社交網(wǎng)絡(luò)賬戶發(fā)布垃圾信息的情況。由于社交網(wǎng)絡(luò)的開放性與即時(shí)性，這些垃圾信息能夠迅速而廣泛的傳播，由垃圾信息傳播而引發(fā)的負(fù)面事件也呈現(xiàn)出越發(fā)嚴(yán)重的趨勢(shì)。因此，針對(duì)專門用于發(fā)布垃圾信息的異常賬戶進(jìn)行識(shí)別與限制，對(duì)減少社交網(wǎng)絡(luò)中的垃圾信息具有重要作用。 本文的貢獻(xiàn)在于將信任度的概念引入社交網(wǎng)絡(luò)中，提出一種計(jì)算模型對(duì)社交網(wǎng)絡(luò)賬戶信任度進(jìn)行評(píng)估，從而根據(jù)評(píng)估結(jié)果對(duì)賬戶進(jìn)行排序。同時(shí)，對(duì)社交網(wǎng)絡(luò)賬戶間關(guān)系進(jìn)行深入挖掘，對(duì)評(píng)估與排序結(jié)果進(jìn)行修正。這種排序不僅可以用于檢測(cè)社交網(wǎng)絡(luò)中的異常賬戶，也可以作為用戶判斷其他賬戶是否可信的依據(jù)。文章主要成果如下： 1）提出基于賬戶特征與行為特征的社交網(wǎng)絡(luò)賬戶信任度計(jì)算模型。論文在賬戶特征、行為特征方面提出多個(gè)能夠用以區(qū)分異常賬戶的特征，引入粗糙集理論的屬性約簡(jiǎn)方法進(jìn)行特征提取，并提出一個(gè)基于數(shù)量分布的特征相似度評(píng)估方法，最后得到賬戶信任度的計(jì)算模型。 2）對(duì)社交網(wǎng)絡(luò)賬戶間關(guān)系與賬戶間交互行為進(jìn)行深入挖掘，，提出AccountRank算法對(duì)賬戶信任度進(jìn)行修正，從而得到更加準(zhǔn)確的結(jié)果。在社交網(wǎng)絡(luò)中，被關(guān)注程度越高的賬戶越值得信任，與值得信任的賬戶交互越多的賬戶越值得信任�；谶@個(gè)現(xiàn)象，本文參考著名的PageRank算法，根據(jù)社交網(wǎng)絡(luò)中賬戶間關(guān)系與賬戶交互行為的特點(diǎn)進(jìn)行修改后得到AccountRank算法，對(duì)所得信任度進(jìn)行修正。 3）以新浪微博為實(shí)驗(yàn)對(duì)象，獲取了大量真實(shí)的數(shù)據(jù)進(jìn)行實(shí)驗(yàn)，以驗(yàn)證模型的有效性。實(shí)驗(yàn)結(jié)果顯示，計(jì)算得到的賬戶信任度能夠用于賬戶的信任排序，為用戶判斷賬戶的可信任程度提供有力依據(jù)。設(shè)定合理的閾值后，能夠?qū)Ξ惓Ｙ~戶進(jìn)行自動(dòng)檢測(cè)。同時(shí)，利用賬戶間關(guān)系對(duì)上述結(jié)果進(jìn)行修正后，相關(guān)指標(biāo)都得到提升。
[Abstract]:Social network is a kind of network service rising in the era of web2.0. It extends offline social activities to online, allowing users to register their accounts and interact on the network. Social networks promote good online social behaviour, but spam is still posted through social network accounts. Due to the openness and immediacy of social networks, these spam information can spread rapidly and widely, and the negative events caused by the dissemination of spam information also show an increasingly serious trend. Therefore, it is very important to identify and restrict the abnormal account which is used to release spam information in social network. The contribution of this paper is to introduce the concept of trust into social networks, and propose a computational model to evaluate the trust of social network accounts, and then sort the accounts according to the evaluation results. At the same time, the relationship between social network accounts is deeply excavated, and the results of evaluation and ranking are revised. This sort can be used not only to detect abnormal accounts in social networks, but also to judge the credibility of other accounts. The main results of this paper are as follows: 1) A social network account trust calculation model based on account feature and behavior feature is proposed. In this paper, a number of features that can be used to distinguish abnormal accounts are proposed in terms of account features and behavioral features, and a feature similarity evaluation method based on quantitative distribution is proposed by introducing the attribute reduction method based on rough set theory for feature extraction. Finally, the calculation model of account trust is obtained. 2) the relationship between social network accounts and the interaction between accounts are deeply mined, and the AccountRank algorithm is proposed to modify the trust degree of the account, so as to get more accurate results. In social networks, accounts with a higher degree of attention are more trustworthy, and accounts that interact with trusted accounts are more trustworthy. Based on this phenomenon, this paper refers to the famous PageRank algorithm, according to the relationship between accounts and the characteristics of account interaction in social networks to modify the characteristics of the AccountRank algorithm, to modify the resulting trust. 3) taking Weibo of Sina as the experimental object, a large number of real data were obtained to verify the validity of the model. The experimental results show that the calculated trust degree can be used to sort the trust of the account and provide a powerful basis for the user to judge the degree of trust of the account. After setting a reasonable threshold, the abnormal account can be automatically detected. At the same time, using the relationship between accounts to revise the above results, the related indicators are improved.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 甘早斌;曾燦;李開;韓建軍;;電子商務(wù)下的信任網(wǎng)絡(luò)構(gòu)造與優(yōu)化[J];計(jì)算機(jī)學(xué)報(bào);2012年01期

2 張宇;于彤;;Mining Trust Relationships from Online Social Networks[J];Journal of Computer Science & Technology;2012年03期

本文編號(hào)：1776476