本文關(guān)鍵詞：社交網(wǎng)絡(luò)中影響最大化的研究與應(yīng)用　出處：《重慶大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 影響最大化 社區(qū)結(jié)構(gòu) 社交網(wǎng)絡(luò) 復(fù)雜網(wǎng)絡(luò)應(yīng)用

【摘要】：在現(xiàn)代互聯(lián)網(wǎng)環(huán)境下,人們逐漸習(xí)慣于社交網(wǎng)絡(luò)生活,并將社交網(wǎng)絡(luò)作為當(dāng)前信息獲取和傳播的主要工具。但是,社交網(wǎng)絡(luò)中的用戶聯(lián)系非常復(fù)雜,難以快速追蹤信息傳播的路徑,加上網(wǎng)絡(luò)實(shí)名認(rèn)證機(jī)制的滯后,使得社交網(wǎng)絡(luò)中經(jīng)常充斥虛假等負(fù)面信息。因此,研究如何利用社交網(wǎng)絡(luò)的特性進(jìn)行合理的輿論引導(dǎo),快速、高效的抑制網(wǎng)絡(luò)謠言的傳播有著重要的應(yīng)用價(jià)值和現(xiàn)實(shí)意義。社交網(wǎng)絡(luò)中個(gè)體通過(guò)交流、分享和推薦信息等互動(dòng)活動(dòng),從而形成具有某一特點(diǎn)的團(tuán)體,如何利用這些社區(qū)等特性挖掘網(wǎng)絡(luò)中影響力最大的節(jié)點(diǎn),并通過(guò)其間的交互作用,使得網(wǎng)絡(luò)中的信息傳播和擴(kuò)散影響范圍最大,從而形成正面的引導(dǎo)是社交網(wǎng)絡(luò)研究的一個(gè)熱點(diǎn)問(wèn)題。本文采用復(fù)雜網(wǎng)絡(luò)的相關(guān)研究技術(shù)建立網(wǎng)絡(luò)中信息傳播的模型,利用社區(qū)網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)對(duì)網(wǎng)絡(luò)中的節(jié)點(diǎn)進(jìn)行影響力最大化的分析,通過(guò)有目標(biāo)的引導(dǎo)方案實(shí)現(xiàn)目標(biāo)輿情的傳播路徑的阻斷或者變更,從而實(shí)現(xiàn)對(duì)輿情的發(fā)展進(jìn)行正確的引導(dǎo)。首先,論文對(duì)影響最大化的研究進(jìn)行了概述,引出了影響函數(shù)的次模屬性,提出基于這個(gè)屬性一種改進(jìn)的貪心爬山算法,克服了傳播函數(shù)在計(jì)算整個(gè)網(wǎng)絡(luò)影響力時(shí)的時(shí)間復(fù)雜度過(guò)高,以及傳播函數(shù)的計(jì)算僅局限于由若干鄰居構(gòu)成的局部子圖時(shí)造成的精確性不足兩個(gè)問(wèn)題。然后,論文根據(jù)社交網(wǎng)絡(luò)的社區(qū)特征能對(duì)信息在網(wǎng)絡(luò)中的傳播過(guò)程的產(chǎn)生重要影響的特性,建立了網(wǎng)絡(luò)節(jié)點(diǎn)平均影響等若干計(jì)算節(jié)點(diǎn)影響的參數(shù),提出了基于社區(qū)結(jié)構(gòu)的影響最大化的計(jì)算模型并通過(guò)實(shí)驗(yàn)對(duì)模型進(jìn)行了驗(yàn)證分析。最后,論文利用該計(jì)算模型設(shè)計(jì)并實(shí)現(xiàn)了基于影響最大化的輿論牽引策略算法,并將之應(yīng)用到社區(qū)輿論引導(dǎo)系統(tǒng)中獲得了較好的引導(dǎo)效果。
[Abstract]:In the modern Internet environment, people are gradually used to the social network life, and the social network as the main tool of information acquisition and dissemination, but the social network user contact is very complex. It is difficult to track the path of information dissemination quickly, coupled with the lag of the real name authentication mechanism, which makes social networks often filled with false and other negative information. It has important application value and practical significance to study how to use the characteristics of social network to guide public opinion and restrain the spread of network rumors quickly and efficiently. Sharing and recommending information and other interactive activities, so as to form a group with a certain characteristics, how to use the characteristics of these communities to mine the most influential nodes in the network, and through the interaction between them. Make the network of information dissemination and diffusion of the largest impact. In order to form positive guidance is a hot issue in the research of social networks. This paper uses the related research technology of complex networks to establish the model of information dissemination in the network. Using the topological structure of the community network to maximize the influence of the nodes in the network through the targeted guiding scheme to achieve the blocking or changing of the transmission path of the target public opinion. In order to achieve the correct guidance of the development of public opinion. Firstly, the thesis summarizes the research of the maximum impact, and leads to the submodule attribute of the influence function. An improved greedy mountain climbing algorithm based on this attribute is proposed, which overcomes the time complexity of the propagation function in calculating the influence of the whole network. And the calculation of propagation function is limited to two problems: the accuracy of local subgraph composed of several neighbors is not enough. According to the social network community characteristics can have an important impact on the transmission of information in the network, the paper established a number of computing node impact parameters such as the average impact of network nodes. Proposed the community structure based on the impact maximization calculation model and through the experiment to carry on the verification analysis to the model. Finally. This paper designs and implements a public opinion traction strategy algorithm based on maximization of influence, and applies it to the community public opinion guidance system to obtain a good guiding effect.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP393.09

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