【摘要】：近年來,隨著互聯(lián)網(wǎng)技術(shù)的日益普及,人們獲取信息的途徑也在發(fā)生著悄然變化,從最初的廣播電視報(bào)紙,到如今的微博貼吧朋友圈。在線社會(huì)網(wǎng)絡(luò)不斷與傳統(tǒng)人際網(wǎng)絡(luò)相融合,產(chǎn)生海量數(shù)據(jù),為社會(huì)網(wǎng)絡(luò)分析帶來了前所未有的機(jī)遇。大批科研工作者對(duì)社會(huì)網(wǎng)絡(luò)影響力最大化、信息傳播規(guī)律等課題進(jìn)行深入研究與分析。其中,如何選擇社會(huì)網(wǎng)絡(luò)中最具影響力的前K個(gè)節(jié)點(diǎn)及如何構(gòu)建準(zhǔn)確的信息傳播模型這兩個(gè)方向,成為了社會(huì)網(wǎng)絡(luò)領(lǐng)域的一個(gè)研究熱點(diǎn)。本文首先在深入分析前人研究的基礎(chǔ)上,針對(duì)現(xiàn)存社會(huì)網(wǎng)絡(luò)影響力最大化算法所存在的問題,引入弱連帶優(yōu)勢理論提出了一種改進(jìn)的中心性算法。其次,詳細(xì)分析了線性閾值模型,結(jié)合社會(huì)網(wǎng)絡(luò)不同節(jié)點(diǎn)間的差異性,引入節(jié)點(diǎn)關(guān)聯(lián)強(qiáng)度的概念及信息自身的引力特性,提出一種新型的社會(huì)網(wǎng)絡(luò)傳播模型。具體的研究內(nèi)容如下:(1)基于社區(qū)結(jié)構(gòu)的關(guān)鍵節(jié)點(diǎn)中心性算法。結(jié)合網(wǎng)絡(luò)社區(qū)結(jié)構(gòu)特性,將邊界節(jié)點(diǎn)及社區(qū)內(nèi)部節(jié)點(diǎn)同時(shí)作為關(guān)鍵節(jié)點(diǎn),來衡量其實(shí)際影響力。根據(jù)弱連帶優(yōu)勢理論,內(nèi)聚性很強(qiáng)的網(wǎng)絡(luò)并不利于節(jié)點(diǎn)獲取外部信息,因此考察連接不同社區(qū)的弱連帶關(guān)系即邊界節(jié)點(diǎn)的屬性,有利于信息的跨區(qū)域傳播;同時(shí),選取社區(qū)內(nèi)部最具影響力節(jié)點(diǎn)可以使信息在社區(qū)內(nèi)快速傳播。二者結(jié)合有利于信息在全網(wǎng)中的擴(kuò)散。本文從3個(gè)不同方面分別在3個(gè)數(shù)據(jù)集上驗(yàn)證了算法的有效性。(2)關(guān)聯(lián)強(qiáng)度閾值模型。通過對(duì)線性閾值模型的深入研究發(fā)現(xiàn),該模型中假設(shè)某一節(jié)點(diǎn)同一時(shí)刻受到來自其鄰接節(jié)點(diǎn)的影響力值均相同。然而,在真實(shí)社會(huì)網(wǎng)絡(luò)中,不同個(gè)體間存在著遠(yuǎn)近親疏的關(guān)系,個(gè)體的差異性決定了節(jié)點(diǎn)受其鄰居節(jié)點(diǎn)影響的差異性;同時(shí),信息的傳播效果與信息自身的吸引力密切相關(guān)。因此,本章提出一種基于LT模型的——關(guān)聯(lián)強(qiáng)度閾值模型。該模型通過吸收線性閾值模型的優(yōu)點(diǎn),結(jié)合社會(huì)網(wǎng)絡(luò)不同節(jié)點(diǎn)間的差異性,引入節(jié)點(diǎn)關(guān)聯(lián)強(qiáng)度的概念及信息自身的引力特性,對(duì)線性閾值模型中的參數(shù)做了改進(jìn)并提出了新的節(jié)點(diǎn)影響力的計(jì)算公式。
[Abstract]:In recent years, with the increasing popularity of Internet technology, people's access to information is also quietly changing, from the original radio and television newspapers, to today's Weibo post bar friends. The combination of online social network and traditional interpersonal network produces massive data and brings an unprecedented opportunity for social network analysis. A large number of researchers deeply study and analyze such topics as maximization of social network influence and rules of information dissemination. Among them, how to select the most influential first K nodes in social network and how to build an accurate information dissemination model have become a research hotspot in the field of social network. In this paper, based on the analysis of previous studies, an improved centrality algorithm is proposed by introducing the weak joint advantage theory in order to solve the problem of the existing algorithms for maximizing the influence of social networks. Secondly, the linear threshold model is analyzed in detail, and a new social network propagation model is proposed by introducing the concept of node association strength and the gravitational properties of information itself, combining with the difference between different nodes of social network. The specific research contents are as follows: (1) the key node centrality algorithm based on community structure. Combined with the characteristics of the network community structure, the boundary node and the community internal node are taken as the key nodes simultaneously to measure their actual influence. According to the theory of weak joint advantage, the strong cohesion of network is not conducive to the node to obtain external information, so the study of the weak link between different communities, that is, the attributes of the boundary node, is conducive to the cross-regional dissemination of information. At the same time, selecting the most influential nodes in the community can make the information spread quickly in the community. The combination of the two is beneficial to the diffusion of information in the whole network. This paper verifies the validity of the algorithm on three data sets from three different aspects. (2) the threshold model of association strength. Through the in-depth study of the linear threshold model, it is found that the model assumes that a node is affected by the same value from its adjacent nodes at the same time. However, in the real social network, there are close and distant relationships between different individuals, and the difference of individuals determines the difference of nodes affected by their neighbors. At the same time, the communication effect of information is closely related to the attraction of information itself. Therefore, in this chapter, an association strength threshold model based on LT model is proposed. By absorbing the advantages of the linear threshold model and combining the differences between different nodes in the social network, this model introduces the concept of node association strength and the gravitational properties of the information itself. The parameters in the linear threshold model are improved and a new formula for calculating nodal influence is proposed.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP301.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 郭靜;曹亞男;周川;張鵬;郭莉;;基于線性閾值模型的影響力傳播權(quán)重學(xué)習(xí)[J];電子與信息學(xué)報(bào);2014年08期

2 李棟;徐志明;李生;劉挺;王秀文;;在線社會(huì)網(wǎng)絡(luò)中信息擴(kuò)散[J];計(jì)算機(jī)學(xué)報(bào);2014年01期

3 薛一波;鮑媛媛;易成岐;;SPNR:社交網(wǎng)絡(luò)中的新型謠言傳播模型[J];信息網(wǎng)絡(luò)安全;2014年01期

4 吳凱;季新生;郭進(jìn)時(shí);劉彩霞;;基于微博網(wǎng)絡(luò)的影響力最大化算法[J];計(jì)算機(jī)應(yīng)用;2013年08期

5 宮秀文;張佩云;;基于PageRank的社交網(wǎng)絡(luò)影響最大化傳播模型與算法研究[J];計(jì)算機(jī)科學(xué);2013年S1期

6 易成岐;鮑媛媛;薛一波;姜京池;;新浪微博的大規(guī)模信息傳播規(guī)律研究[J];計(jì)算機(jī)科學(xué)與探索;2013年06期

7 潘新;鄧貴仕;佟斌;;基于社會(huì)網(wǎng)絡(luò)的輿情傳播模型構(gòu)建與分析[J];運(yùn)籌與管理;2011年02期

相關(guān)碩士學(xué)位論文 前2條

1 陳浩;基于閾值的社會(huì)網(wǎng)絡(luò)影響力最大化算法[D];復(fù)旦大學(xué);2012年

2 田家堂;在線社會(huì)網(wǎng)絡(luò)中影響最大化問題的研究[D];復(fù)旦大學(xué);2012年

，

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