【摘要】：建立合理的社會(huì)網(wǎng)絡(luò)節(jié)點(diǎn)(成員)影響力評(píng)估體系、識(shí)別影響力節(jié)點(diǎn),是分析一個(gè)網(wǎng)絡(luò)組織結(jié)構(gòu)的關(guān)鍵問(wèn)題,對(duì)于研究網(wǎng)絡(luò)中節(jié)點(diǎn)之間的影響力分布乃至傳播模式都具有重要意義。對(duì)節(jié)點(diǎn)影響力的評(píng)估不僅是網(wǎng)絡(luò)輿情引導(dǎo)者決策時(shí)的重要信息來(lái)源,也是實(shí)施網(wǎng)絡(luò)病毒營(yíng)銷時(shí)選擇種子節(jié)點(diǎn)的重要依據(jù)。常見(jiàn)的識(shí)別方法是通過(guò)比較節(jié)點(diǎn)中心性測(cè)度數(shù)值的大小,來(lái)獲得最具影響力的節(jié)點(diǎn)。然而每一種中心性測(cè)度都有自己的優(yōu)勢(shì)和劣勢(shì)。TOPSIS作為一種多屬性決策手段已經(jīng)成為決策的一個(gè)重要分支,而將TOPSIS用來(lái)識(shí)別網(wǎng)絡(luò)中的影響力節(jié)點(diǎn)尚不多見(jiàn)。本文選取具有代表性的多屬性決策方法--TOPSIS方法作為主要研究方法,提出基于熵權(quán)的TOPSIS拓展方法,并將新方法用于社會(huì)網(wǎng)絡(luò)節(jié)點(diǎn)影響力評(píng)估的模型中。論文的主要內(nèi)容有：首先,闡述了論文的研究背景和意義,并對(duì)網(wǎng)絡(luò)營(yíng)銷和社會(huì)網(wǎng)絡(luò)影響力最大化研究的現(xiàn)狀進(jìn)行了文獻(xiàn)綜述,并提出了論文總的研究思路和框架。然后,論文介紹了網(wǎng)絡(luò)營(yíng)銷的背景知識(shí)、TOPSIS方法的理論基礎(chǔ)和具體計(jì)算步驟,系統(tǒng)介紹了社會(huì)網(wǎng)絡(luò)影響力評(píng)估的常見(jiàn)方法,包括影響力節(jié)點(diǎn)識(shí)別方法和影響力最大化問(wèn)題。接著,論文對(duì)社會(huì)網(wǎng)絡(luò)影響力做了界定,并分析了影響力的相關(guān)因素包括時(shí)間、節(jié)點(diǎn)位置和話題等,比較了幾個(gè)常見(jiàn)影響力最大化問(wèn)題的模型,包括貪心算法、線性閥值模型和獨(dú)立級(jí)聯(lián)模型等。在介紹了常見(jiàn)網(wǎng)絡(luò)分析中心性方法后,重點(diǎn)對(duì)TOPSIS方法展開(kāi)了研究和拓展。本文使用熵權(quán)法來(lái)確定TOPSIS方法中多種屬性的權(quán)重,提出了基于熵權(quán)的TOPSIS拓展方法�；陟貦�(quán)的TOPSIS法首先對(duì)社會(huì)網(wǎng)絡(luò)的多屬性,即網(wǎng)絡(luò)中節(jié)點(diǎn)的多種不同中心性測(cè)度進(jìn)行屬性權(quán)重的確定,再用經(jīng)典TOPSIS法來(lái)集成這些多屬性,從而獲得每個(gè)節(jié)點(diǎn)的重要性估值及其排名,達(dá)到識(shí)別網(wǎng)絡(luò)中影響力節(jié)點(diǎn)的目標(biāo)。將網(wǎng)絡(luò)節(jié)點(diǎn)不同的中心性測(cè)度作為TOPSIS多屬性的新方法在一定程度上克服了單一中心性測(cè)度方法的局限性和劣勢(shì),而熵權(quán)法的引入則是增強(qiáng)了TOPSIS方法的客觀性。緊接著論文通過(guò)對(duì)歷史文獻(xiàn)的比較參考,選取了具有典型意義的社會(huì)網(wǎng)絡(luò)分析中心性指標(biāo)。最后,我們用Susceptible-Infected (SI)模型來(lái)比較新方法和常見(jiàn)的單一中心性測(cè)度方法的性能。數(shù)據(jù)實(shí)驗(yàn)結(jié)果顯示了新方法相比常見(jiàn)方法的高效性和可行性。這將為節(jié)點(diǎn)識(shí)別研究在輿情控制、病毒營(yíng)銷等方面的應(yīng)用節(jié)約時(shí)間成本,從而達(dá)到更好的效果。
[Abstract]:Establishing a reasonable social network node (member) influence evaluation system and identifying the influence nodes is the key problem for analyzing a network organization structure. It is of great significance to the study of the distribution of influence and even the mode of communication among nodes in the network. The evaluation of the influence of the nodes is not only the heavy duty of the network public opinion guide. The source of information is also an important basis for the selection of seed nodes in the network virus marketing. The common method of recognition is to obtain the most influential nodes by comparing the size of the node centrality measure value. However, each central measure has its own advantages and disadvantages.TOPSIS as a multi attribute decision-making method. As an important branch of decision making, it is still rare to use TOPSIS to identify the influence nodes in the network. In this paper, a representative multi attribute decision making method --TOPSIS method is selected as the main research method, and the TOPSIS extension method based on entropy weight is proposed, and the new method is used in the model of the social network node influence evaluation. The main contents are as follows: first, it expounds the research background and significance of the paper, and reviews the current status of the research on network marketing and social network influence maximization, and puts forward the general research ideas and framework of the paper. Then, the paper introduces the background knowledge of network marketing, the theoretical basis and concrete calculation step of the TOPSIS method. The common methods of social network impact assessment are introduced, including the identification of influence nodes and the maximization of influence. Then, the thesis defines the influence of social network, and analyzes the factors related to the influence, including time, node position and topic, and compares the models of several common influence maximization problems. Type, including greedy algorithm, linear threshold model and independent cascade model. After introducing the central method of common network analysis, this paper focuses on the research and expansion of the TOPSIS method. In this paper, entropy weight method is used to determine the weight of multiple attributes in the TOPSIS method, and the TOPSIS extension method based on entropy weight is proposed. The entropy weight based TOPSIS head method is proposed. The multiple attributes of the social network, that is, to determine the attribute weights of a variety of different centrality measures of the nodes in the network, and then integrate these multiple attributes with the classical TOPSIS method, obtain the importance valuation and ranking of each node, and achieve the target of identifying the influence nodes in the network. The central measure of the network nodes is taken as the central measure. The new method of TOPSIS multi attributes overcomes the limitation and disadvantage of the single central measure method to a certain extent, and the introduction of entropy weight method is to enhance the objectivity of the TOPSIS method. After the comparison of the historical literature, the paper selects the central index of the social network analysis with typical significance. Finally, we use Sus The ceptible-Infected (SI) model compares the performance of the new method and the common single centrality measurement method. The results of the data experiment show the efficiency and feasibility of the new method compared with the common methods. This will save time and cost for the application of node recognition in public opinion control, virus marketing and so on, thus achieving better results.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F224

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本文編號(hào)：2155870