本文選題：社交網(wǎng)絡(luò)　切入點：SDIR信息傳播模型　出處：《蘭州理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來隨著互聯(lián)網(wǎng)技術(shù)的突飛猛進,在線社交網(wǎng)絡(luò)正在逐步改變?nèi)藗兊男畔@取方式與社交方式。以美國的Facebook、Twitter、Youtube,以及國內(nèi)的新浪微博、QQ、微信等為代表的大量在線社交網(wǎng)絡(luò)發(fā)展勢頭迅猛,已經(jīng)累計了數(shù)以億計的用戶,影響力巨大。社交網(wǎng)絡(luò)以其實時性、功能性、社交性等諸多優(yōu)勢成為web2.0體系結(jié)構(gòu)下最重要的應(yīng)用之一,對現(xiàn)在社會中各種新聞事件、不實謠言、群眾輿論等信息的傳播有重要影響,利用好社交網(wǎng)絡(luò)這種新興工具可以為人類創(chuàng)造極大的價值,但同時社交網(wǎng)絡(luò)在信息傳播上的優(yōu)勢也可能被不當利用而造成巨大的危害。因此以社交網(wǎng)絡(luò)為研究對象的信息傳播研究具有十分重要的意義,也是近年來社會計算領(lǐng)域的一個熱點。目前社交網(wǎng)絡(luò)方面的研究焦點之一是以經(jīng)典的傳染病動力學(xué)模型為基礎(chǔ),來挖掘特定網(wǎng)絡(luò)上的信息傳播規(guī)律。本文首先介紹了社交網(wǎng)絡(luò)的發(fā)展現(xiàn)狀與研究意義,陳列出研究所涉及的復(fù)雜網(wǎng)絡(luò)與信息傳播的基礎(chǔ)知識;然后針對社交網(wǎng)絡(luò)中信息傳播的特點,在傳統(tǒng)的SIR模型基礎(chǔ)上,通過加入新的一類假免疫節(jié)點建立了SDIR模型;最后在此基礎(chǔ)上考慮到鄰居節(jié)點間的相互影響,定義了三個傳播概率函數(shù),對SDIR模型做了改進。通過對比不同條件下信息傳播的過程,實驗證明了信息不能覆蓋全網(wǎng)絡(luò),且Twitter比新浪微博有更好的信息傳播效率的推測,并發(fā)現(xiàn)初始傳播概率會對信息傳播有重要影響。在上述仿真模擬SDIR模型的過程中,必須提前以經(jīng)驗值設(shè)定基本的傳播概率,才可以運用各種傳播模型模擬信息的傳播過程。然而本文通過分析發(fā)現(xiàn)人為設(shè)定的傳播概率對精確描述傳播過程有很大影響,故首先以知識圖譜補全問題中路徑排序算法的思想為基礎(chǔ),提出了一種通過隨機游走來計算節(jié)點影響力的算法;然后在節(jié)點影響力的基礎(chǔ)上通過歸一化得到初始的信息傳播概率。實驗對比了固定傳播概率與考慮了信息源節(jié)點影響力的傳播概率對傳播結(jié)果造成的差異,通過證明影響力算法的有效性,驗證出計算后的傳播概率更加符合真實情況,并將通過計算得到的傳播概率與SDIR模型結(jié)合,進一步完善了本文提出的信息傳播模型。
[Abstract]:In recent years, with the rapid development of Internet technology, Online social networks are gradually changing the way people access information and how they socialize. A large number of online social networks, such as Facebook Twitter Youtubein the United States, and Sina Weibo QQQ, WeChat in China, are developing rapidly, and have accumulated hundreds of millions of users. Social network has become one of the most important applications under the web2.0 architecture because of its real-time, functional and social advantages. The dissemination of public opinion and other information has an important impact. Making good use of social networks as a new tool can create great value for human beings. But at the same time, the advantage of social network in information dissemination may also be improperly used to cause great harm. Therefore, the study of information dissemination based on social network is of great significance. It is also a hot topic in the field of social computing in recent years. At present, one of the research focuses on social networks is based on the classical dynamics model of infectious diseases. Firstly, this paper introduces the development status and research significance of social network, and displays the basic knowledge of complex network and information dissemination. Then, according to the characteristics of information transmission in social networks, based on the traditional SIR model, the SDIR model is established by adding a new class of pseudo-immune nodes. Three propagation probability functions are defined, and the SDIR model is improved. By comparing the process of information transmission under different conditions, the experiment proves that the information can not cover the whole network, and Twitter has better information transmission efficiency than Sina Weibo. It is also found that the initial propagation probability has an important effect on the information transmission. In the process of simulating the SDIR model mentioned above, the basic propagation probability must be set in advance with the experience value. However, through the analysis, it is found that the artificially set propagation probability has a great influence on the accurate description of the propagation process. Therefore, based on the idea of path sorting algorithm in the knowledge map complement problem, this paper proposes an algorithm to calculate the influence of nodes by random walk. Then the initial information propagation probability is obtained by normalization on the basis of node influence, and the difference between the fixed propagation probability and the transmission probability considering the influence of the information source node is compared. By proving the validity of the influence algorithm, it is verified that the calculated propagation probability is more in line with the real situation, and the information propagation model proposed in this paper is further improved by combining the calculated propagation probability with the SDIR model.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G206;TP393.09

【參考文獻】

相關(guān)期刊論文 前7條

1 王金龍;劉方愛;朱振方;;一種基于用戶相對權(quán)重的在線社交網(wǎng)絡(luò)信息傳播模型[J];物理學(xué)報;2015年05期

2 曹玖新;吳江林;石偉;劉波;鄭嘯;羅軍舟;;新浪微博網(wǎng)信息傳播分析與預(yù)測[J];計算機學(xué)報;2014年04期

3 吳信東;李毅;李磊;;在線社交網(wǎng)絡(luò)影響力分析[J];計算機學(xué)報;2014年04期

4 王輝;韓江洪;鄧林;程克勤;;基于移動社交網(wǎng)絡(luò)的謠言傳播動力學(xué)研究[J];物理學(xué)報;2013年11期

5 顧亦然;夏玲玲;;在線社交網(wǎng)絡(luò)中謠言的傳播與抑制[J];物理學(xué)報;2012年23期

6 張彥超;劉云;張海峰;程輝;熊菲;;基于在線社交網(wǎng)絡(luò)的信息傳播模型[J];物理學(xué)報;2011年05期

7 冀進朝;韓笑;王U，

本文編號：1590173