【摘要】：社會(huì)網(wǎng)絡(luò)是由社會(huì)行為者即個(gè)人或者組織,社交關(guān)系以及行為者之間的其他社交交互組成的社會(huì)結(jié)構(gòu),在社會(huì)科學(xué)中用于研究個(gè)人、團(tuán)體、組織甚至整個(gè)社會(huì)之間的關(guān)系。隨著在線社交網(wǎng)絡(luò)的發(fā)展,信息產(chǎn)生和傳播的成本大大下降,信息的數(shù)量呈幾何倍數(shù)的增長。這些網(wǎng)絡(luò)中每天產(chǎn)生的巨大數(shù)據(jù)具有海量、高維、半結(jié)構(gòu)化等明顯的特征,這些特征可以直接反映人類社會(huì)的真實(shí)活動(dòng)規(guī)律,所以社會(huì)網(wǎng)絡(luò)逐漸成為多領(lǐng)域研究者的研究熱點(diǎn)。社會(huì)網(wǎng)絡(luò)分析最初作為社會(huì)學(xué)研究的一個(gè)分支,后來逐漸在數(shù)學(xué)、社會(huì)科學(xué)、人類學(xué)、生物學(xué)、通信科學(xué)等領(lǐng)域發(fā)展起來。社會(huì)網(wǎng)絡(luò)具有高度動(dòng)態(tài)性,可能導(dǎo)致節(jié)點(diǎn)和邊在未來某個(gè)時(shí)刻出現(xiàn)或消失。因此,在社會(huì)網(wǎng)絡(luò)鏈接預(yù)測(cè)中預(yù)測(cè)當(dāng)前網(wǎng)絡(luò)中缺失的邊和新的或消失的未來網(wǎng)絡(luò)中的邊,對(duì)于挖掘社會(huì)網(wǎng)絡(luò)中的未知信息和分析社會(huì)網(wǎng)絡(luò)的演化是重要的。傳統(tǒng)基于網(wǎng)絡(luò)拓?fù)涞墓?jié)點(diǎn)相似性預(yù)測(cè)鏈接變化的方法很少考慮用戶產(chǎn)生消息內(nèi)容本身的特征,本文在傳統(tǒng)社會(huì)網(wǎng)絡(luò)鏈接預(yù)測(cè)方法的基礎(chǔ)上引入用戶產(chǎn)生的內(nèi)容信息,利用轉(zhuǎn)移熵量化用戶對(duì)之間信息的轉(zhuǎn)移作為用戶之間相似性的一個(gè)特征,然后本文利用信息轉(zhuǎn)移特征與拓?fù)涮卣鞯母鞣N線性組合定義了三種鏈接預(yù)測(cè)方法。本文分別在基于LDA模型主題向量表示內(nèi)容和分布式詞向量表示內(nèi)容下針對(duì)上述三種鏈接預(yù)測(cè)方法進(jìn)行實(shí)驗(yàn),驗(yàn)證信息轉(zhuǎn)移對(duì)鏈接預(yù)測(cè)的影響并比較基于信息轉(zhuǎn)移的鏈接預(yù)測(cè)方法與幾個(gè)經(jīng)典傳統(tǒng)鏈接預(yù)測(cè)算法。在實(shí)驗(yàn)中發(fā)現(xiàn)了結(jié)合了信息轉(zhuǎn)移的網(wǎng)絡(luò)在社會(huì)網(wǎng)絡(luò)鏈接預(yù)測(cè)中具有更加符合真實(shí)社交網(wǎng)絡(luò)、更好的鏈接預(yù)測(cè)性能,在社會(huì)網(wǎng)絡(luò)分析中具有一定的優(yōu)勢(shì)。
[Abstract]:Social network is a social structure composed of individual or organization, social relations and other social interactions between social actors. It is used in social sciences to study the relationships among individuals, groups, organizations and even the whole society. With the development of online social networks, the cost of information generation and dissemination has been greatly reduced, and the amount of information has increased exponentially. The huge data generated every day in these networks have the obvious characteristics of massive, high-dimensional, semi-structured and so on. These characteristics can directly reflect the real activities of human society, so the social network has gradually become the research hotspot of multi-field researchers. As a branch of sociological research, social network analysis has gradually developed in the fields of mathematics, social science, anthropology, biology, communication science and so on. Social networks are highly dynamic and may cause nodes and edges to appear or disappear at some point in the future. Therefore, it is important to predict the missing edges in the current network and the edges in the new or vanishing future networks in the prediction of social network links for mining unknown information in social networks and analyzing the evolution of social networks. The traditional method of node similarity prediction based on network topology rarely considers the characteristics of user-generated message content itself. This paper introduces user-generated content information based on the traditional social network link prediction method. The transfer entropy is used to quantify the information transfer between users as a feature of the similarity between users. Then three link prediction methods are defined by using various linear combinations of information transfer features and topological features. In this paper, the above three link prediction methods are tested based on LDA model subject vector representation and distributed word vector representation, respectively. The effect of information transfer on link prediction is verified and several classical link prediction algorithms based on information transfer are compared. In the experiment, it is found that the network combined with information transfer has more realistic social network and better link prediction performance in the social network link prediction, and it has some advantages in social network analysis.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09

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本文編號(hào)：2406379