【摘要】：近幾年,社交網(wǎng)絡(luò)在互聯(lián)網(wǎng)中的地位越來越重要,已經(jīng)被廣泛地進(jìn)行了研究,因?yàn)槿藗兏敢庠谏缃痪W(wǎng)絡(luò)中分享他們的想法和心情狀態(tài),社交網(wǎng)絡(luò)中蘊(yùn)藏著大量有價(jià)值的信息,利用社交網(wǎng)絡(luò)可以進(jìn)行許多的商業(yè)活動(dòng),例如廣告、輿情分析、信息傳播等。其中從社交網(wǎng)絡(luò)中挖掘有限的一些用戶,利用這些用戶進(jìn)行商品推廣和信息傳播正變得越來越熱門,已經(jīng)形成了一類研究問題—社交網(wǎng)絡(luò)影響力最大化。社交網(wǎng)絡(luò)影響力最大化是這樣一類問題,在社交網(wǎng)絡(luò)中識(shí)別一些最有影響力的人,這些人作為初始的傳播信息的源頭,可以將信息傳播到最多的人。然而,現(xiàn)有的方法都忽略了社交網(wǎng)絡(luò)中人的興趣因素,這些方法和模型是不合理的。因?yàn)楝F(xiàn)實(shí)中人會(huì)有多個(gè)興趣,并且對(duì)每個(gè)興趣的敏感程度也不一樣。另外,這些方法也忽略了要傳播的信息的內(nèi)容,因?yàn)椴煌尘暗娜藢?duì)不同的信息表現(xiàn)也不一樣,所以同樣的人群對(duì)于不同的信息有著不同的影響力。本文針對(duì)已有的研究工作,指出了這些工作中的不足和缺陷,主要集中在已有的工作沒有考慮到用戶的興趣因素,同時(shí)也沒有考慮到要傳播的信息的內(nèi)容,以至于挖掘出來的有限的用戶并不能夠使傳播信息的影響力最大化。本文解決了上述兩個(gè)主要問題,結(jié)合之前的研究工作,對(duì)社交網(wǎng)絡(luò)影響力最大化重新進(jìn)行了定義,提出了攜帶興趣組的社交網(wǎng)絡(luò)影響力最大化的概念,設(shè)計(jì)了一種方法把社交網(wǎng)絡(luò)里的興趣組識(shí)別出來,并且結(jié)合興趣組的概念,提出了一種新的衡量多興趣組社交網(wǎng)絡(luò)影響力的方法,最終提出了一個(gè)新奇的IING(Identifying Influential Nodes Greedy Algorithm)算法來計(jì)算最有影響力的用戶,IING算法能夠使挖掘到的一些用戶作為初始的信息傳播源時(shí),信息能夠被更多的人接受。最后,本文對(duì)提出的識(shí)別社交網(wǎng)絡(luò)中的興趣組的方法在真實(shí)的數(shù)據(jù)集上進(jìn)行了實(shí)驗(yàn),證明了方法的有效性。并且針對(duì)最終提出的IING算法進(jìn)行了大量的實(shí)驗(yàn)驗(yàn)證,實(shí)驗(yàn)結(jié)果表明,本文提出的IING算法在時(shí)間上和效果上都優(yōu)于現(xiàn)有的方法。
[Abstract]:In recent years, the status of social networks in the Internet has become increasingly important and has been extensively studied, because people are more willing to share their thoughts and mood states in social networks, which contain a lot of valuable information. Social networks can be used for many business activities, such as advertising, public opinion analysis, information dissemination, and so on. Among them, it is becoming more and more popular to mine a limited number of users from social networks, using these users to promote goods and spread information, which has formed a kind of research problem-maximizing the influence of social networks. Maximizing the influence of social networks is a problem in which some of the most influential people are identified, who, as the source of initial dissemination of information, can spread information to the largest number of people. However, the existing methods ignore the human interest in social networks, and these methods and models are unreasonable. Because in reality people have more than one interest, and the sensitivity to each interest is different. In addition, these methods also ignore the content of the information to be disseminated, because different people from different backgrounds have different information performance, so the same people have different influence on different information. Aiming at the existing research work, this paper points out the shortcomings and shortcomings of these work, which mainly focus on the fact that the existing work does not take into account the interest of the user, nor the content of the information to be disseminated at the same time. So that the limited users excavated can not maximize the impact of the dissemination of information. This paper solves the above two main problems, combines the previous research work, redefines the social network influence maximization, and puts forward the concept of the social network influence maximization with interest group. This paper designs a method to identify interest groups in social networks, and proposes a new method to measure the influence of multi-interest groups social networks by combining the concept of interest groups. Finally, a novel IING (Identifying Influential Nodes Greedy Algorithm) algorithm is proposed to calculate the most influential users. The IING algorithm can make the information accepted by more people when some users are used as the initial source of information propagation. Finally, the proposed method for identifying interest groups in social networks is tested on real data sets, and the effectiveness of the method is proved. The experimental results show that the proposed IING algorithm is superior to the existing methods in time and effect.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 李國(guó)良;楚婭萍;馮建華;徐堯強(qiáng);;多社交網(wǎng)絡(luò)的影響力最大化分析[J];計(jì)算機(jī)學(xué)報(bào);2016年04期

2 陳浩;王軼彤;;基于閾值的社交網(wǎng)絡(luò)影響力最大化算法[J];計(jì)算機(jī)研究與發(fā)展;2012年10期

3 田家堂;王軼彤;馮小軍;;一種新型的社會(huì)網(wǎng)絡(luò)影響最大化算法[J];計(jì)算機(jī)學(xué)報(bào);2011年10期

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本文編號(hào)：2415083