基于社區(qū)結構的網(wǎng)絡影響力傳播算法研究
發(fā)布時間：2018-10-26 08:01
【摘要】：近年來,隨著互聯(lián)網(wǎng)技術的日益普及,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紙,到如今的微博貼吧朋友圈。在線社會網(wǎng)絡不斷與傳統(tǒng)人際網(wǎng)絡相融合,產(chǎn)生海量數(shù)據(jù),為社會網(wǎng)絡分析帶來了前所未有的機遇。大批科研工作者對社會網(wǎng)絡影響力最大化、信息傳播規(guī)律等課題進行深入研究與分析。其中,如何選擇社會網(wǎng)絡中最具影響力的前K個節(jié)點及如何構建準確的信息傳播模型這兩個方向,成為了社會網(wǎng)絡領域的一個研究熱點。本文首先在深入分析前人研究的基礎上,針對現(xiàn)存社會網(wǎng)絡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的中心性算法。其次,詳細分析了線性閾值模型,結合社會網(wǎng)絡不同節(jié)點間的差異性,引入節(jié)點關聯(lián)強度的概念及信息自身的引力特性,提出一種新型的社會網(wǎng)絡傳播模型。具體的研究內容如下:(1)基于社區(qū)結構的關鍵節(jié)點中心性算法。結合網(wǎng)絡社區(qū)結構特性,將邊界節(jié)點及社區(qū)內部節(jié)點同時作為關鍵節(jié)點,來衡量其實際影響力。根據(jù)弱連帶優(yōu)勢理論,內聚性很強的網(wǎng)絡并不利于節(jié)點獲取外部信息,因此考察連接不同社區(qū)的弱連帶關系即邊界節(jié)點的屬性,有利于信息的跨區(qū)域傳播;同時,選取社區(qū)內部最具影響力節(jié)點可以使信息在社區(qū)內快速傳播。二者結合有利于信息在全網(wǎng)中的擴散。本文從3個不同方面分別在3個數(shù)據(jù)集上驗證了算法的有效性。(2)關聯(lián)強度閾值模型。通過對線性閾值模型的深入研究發(fā)現(xiàn),該模型中假設某一節(jié)點同一時刻受到來自其鄰接節(jié)點的影響力值均相同。然而,在真實社會網(wǎng)絡中,不同個體間存在著遠近親疏的關系,個體的差異性決定了節(jié)點受其鄰居節(jié)點影響的差異性;同時,信息的傳播效果與信息自身的吸引力密切相關。因此,本章提出一種基于LT模型的——關聯(lián)強度閾值模型。該模型通過吸收線性閾值模型的優(yōu)點,結合社會網(wǎng)絡不同節(jié)點間的差異性,引入節(jié)點關聯(lián)強度的概念及信息自身的引力特性,對線性閾值模型中的參數(shù)做了改進并提出了新的節(jié)點影響力的計算公式。
[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.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP301.6
本文編號：2295072
[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.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP301.6
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