本文選題：K-Shell　切入點：社交網(wǎng)絡(luò)　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著擁有大量用戶群體的社交網(wǎng)絡(luò)不斷興起,人們信息交流的方式也正悄悄改變。社交網(wǎng)絡(luò)這種方便、靈活的交互方式,使得信息的產(chǎn)生、發(fā)酵更為快捷,信息的擴散時間也大大縮短,信息演化、傳遞的復(fù)雜程度與不確定性也隨之大大增加,這對于信息傳播的監(jiān)控、輿論輿情的引導(dǎo)構(gòu)成了嚴(yán)峻的挑戰(zhàn)。因此,如何識別出社交網(wǎng)絡(luò)中具有較大影響力的用戶主體,進而掌握社交網(wǎng)絡(luò)中信息的傳遞導(dǎo)向以及輿論的演化趨勢,成為了現(xiàn)在階段亟待解決的問題。本文結(jié)合復(fù)雜網(wǎng)絡(luò)理論、傳染病動力學(xué)等學(xué)科的思想與方法,對影響力主體識別算法K-Shell算法進行了深入的研究與探討,并在此基礎(chǔ)上提出了一種新的算法。論文的研究工作得到了國家自然科學(xué)基金項目(No.61271308、61172072、61401015)和北京市教育委員會研究生學(xué)科建設(shè)項目的支持。論文主要工作如下:首先,本文詳細分析了 K-Shell算法的分解原理與計算步驟,發(fā)現(xiàn)K-Shell算法在計算網(wǎng)絡(luò)中節(jié)點的影響力大小時,雖然考慮了節(jié)點的自身屬性與位置屬性,卻忽略了節(jié)點的局域?qū)傩?這導(dǎo)致K-Shell算法將網(wǎng)絡(luò)按照不同的k-shell值劃分為若干層后,每一層所包含的節(jié)點數(shù)量較多,使得網(wǎng)絡(luò)的劃分結(jié)果變得粗�；�,而且無法橫向比較相同k-shell層中節(jié)點之間的影響力大小。本文在此基礎(chǔ)上提出了基于權(quán)重的 K-Shell 改進算法(Weighted K-Shell Algorithm),簡稱 WKS 算法。WKS算法綜合考慮了節(jié)點的自身屬性、位置屬性和局域?qū)傩?用邊的潛在影響力來度量節(jié)點的局域?qū)傩?從而將無權(quán)網(wǎng)絡(luò)問題轉(zhuǎn)化為加權(quán)網(wǎng)絡(luò)問題。隨后,本文使用Python語言編程實現(xiàn)了 K-Shell算法與WKS算法對網(wǎng)絡(luò)的分解步驟,并在真實社交網(wǎng)絡(luò)——新浪微博用戶關(guān)系網(wǎng)絡(luò)中進行了模擬仿真,然后將兩種算法的仿真結(jié)果進行了詳細對比分析,最終的結(jié)果表明用戶節(jié)點的Wk-shell值越大,其影響力越大,而且WKS算法對網(wǎng)絡(luò)節(jié)點影響力大小的劃分結(jié)果比K-Shell算法粒度更加細膩,實用性更高。最后本文借助SIR信息傳播模型對WKS算法的有效性與準(zhǔn)確性進行驗證,在真實社交網(wǎng)絡(luò)中的仿真結(jié)果表明,WKS算法識別社交網(wǎng)絡(luò)影響力主體的結(jié)果準(zhǔn)確性高。該方法能夠為社交網(wǎng)絡(luò)中的輿情控制、廣告營銷等應(yīng)用領(lǐng)域提供理論支持。
[Abstract]:With the rise of social networks with a large number of users, the way people communicate information is changing quietly. The convenient and flexible way of interacting with social networks makes the production of information faster. The diffusion time of information is shortened greatly, the information evolves, the complexity and uncertainty of transmission increase greatly, which poses a severe challenge to the monitoring of information dissemination and the guidance of public opinion. How to identify the user who has great influence in social network and how to master the direction of information transmission and the evolution trend of public opinion in social network has become a problem to be solved in the present stage. This paper combines the theory of complex network. Based on the ideas and methods of infectious disease dynamics and other subjects, the K-Shell algorithm for the identification of influential agents is deeply studied and discussed. On the basis of this, a new algorithm is proposed. The research work of this paper is supported by the National Natural Science Foundation Project No. 61271308 (61172072) and the postgraduate subject construction project of Beijing Municipal Commission of Education. The main work of this paper is as follows: first of all, In this paper, the decomposition principle and calculation steps of K-Shell algorithm are analyzed in detail. It is found that in calculating the influence of nodes in the network, K-Shell algorithm takes into account the attributes of nodes themselves and location, but neglects the local attributes of nodes. This result in K-Shell algorithm divides the network into several layers according to different k-shell values, each layer contains a large number of nodes, which makes the network partition results become coarse grained. Moreover, the influence between nodes in the same k-shell layer can not be compared horizontally. Based on this, an improved K-shell algorithm based on weight is proposed in this paper, which is called WKS algorithm. WKS algorithm takes into account the attributes of nodes. The location attribute and the local attribute measure the local property of the node with the potential influence of the edge, thus transforming the unauthorized network problem into the weighted network problem. In this paper, the decomposing steps of K-Shell algorithm and WKS algorithm to the network are realized by using Python language, and the simulation is carried out in the real social network-Sina Weibo user relationship network. Then the simulation results of the two algorithms are compared and analyzed in detail. The final results show that the greater the Wk-shell value of the user node, the greater its influence, and the finer the granularity of the WKS algorithm is compared with the K-shell algorithm. Finally, this paper verifies the validity and accuracy of WKS algorithm with the help of SIR information transmission model. The simulation results in real social networks show that the WKS algorithm has high accuracy in identifying the influential agents of social networks, and this method can provide theoretical support for the applications of public opinion control and advertising marketing in social networks.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09;O157.5

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