【摘要】：瞬態(tài)社會網(wǎng)絡是指在特定時間、為特定事件、持續(xù)時間短及面對面接觸所形成的社會網(wǎng)絡。不同于在線社會網(wǎng)絡,瞬態(tài)社會網(wǎng)絡由于其特征,能夠提供更加安全可靠的信息,但是瞬態(tài)社會網(wǎng)絡也有自己的缺點,持續(xù)時間太短,稍縱即逝,對于這樣壽命很短卻有效的社會網(wǎng)絡,在其中做信息擴散的研究是一件很有意義的事情,但是,對于這種社會網(wǎng)絡中信息擴散的研究,傳統(tǒng)的方法直接用在其中已經(jīng)是不可能的事情了,因此,怎樣依據(jù)在線社會網(wǎng)絡中的信息擴散的研究成果來研究瞬態(tài)社會網(wǎng)絡中的信息擴散也就成了研究的方向。本文首先對瞬態(tài)社會網(wǎng)絡特征及研究現(xiàn)狀進行了分析,指出了已有的研究成果并不能完全考慮社會網(wǎng)絡的特征。然后,對信息擴散及信息擴散最大化的相關(guān)進展進行了較為詳實的介紹與分析。針對瞬態(tài)社會網(wǎng)絡中信息擴散的問題,提出了一個基于結(jié)構(gòu)洞的信息擴散模型。首先,現(xiàn)有的方法通常是針對在線社會網(wǎng)絡的,并不能直接運用到瞬態(tài)社會網(wǎng)絡中。其次,結(jié)合在線社會網(wǎng)絡中的信息擴散模型,是否有能夠滿足瞬態(tài)社會網(wǎng)絡特征的模型。最后,以真實數(shù)據(jù)集為基礎(chǔ),進行實驗驗證模型的正確型與有效性。瞬態(tài)社會網(wǎng)絡的易變性導致自身的存在時間非常短,如果就某一時刻的瞬態(tài)社會網(wǎng)絡進行研究太過狹窄,我們就結(jié)合結(jié)構(gòu)洞,整合了所有時刻的瞬態(tài)社會網(wǎng)絡組成全局瞬態(tài)社會網(wǎng)絡,這樣的話,我們就可以像沿用在線社會網(wǎng)絡的方法進行研究。因此,瞬態(tài)網(wǎng)絡與其他的任何網(wǎng)絡一樣,只要能夠找到網(wǎng)絡的特征,就能夠改進我們的模型以適用瞬態(tài)社會網(wǎng)絡。隨后,我們繼續(xù)進行信息擴散最大化的研究,針對瞬態(tài)網(wǎng)絡中的相遇時間和相遇次數(shù)特征來進行分析。首先,提出了瞬態(tài)社會網(wǎng)絡中的相關(guān)概念并給出了問題的定義。然后,設(shè)計基于相遇次數(shù)和相遇時間來確定節(jié)點的影響力,在全局瞬態(tài)社會網(wǎng)絡下,找到影響力最大的節(jié)點集合并作為初始受眾,有效的解決了瞬態(tài)社會網(wǎng)絡中信息擴散最大化問題。最后,在兩個真實數(shù)據(jù)集上進行了實驗,驗證所提出算法的可行性及有效性。
[Abstract]:Transient social network is a kind of social network formed by specific events, short duration and face-to-face contact at a particular time. Unlike online social networks, transient social networks can provide more secure and reliable information because of their characteristics, but transient social networks also have their own shortcomings, too short duration, fleeting, It is very meaningful to study the diffusion of information in such a short but effective social network, but for the study of the diffusion of information in such a social network, It is impossible for traditional methods to be directly used among them. Therefore, how to study the diffusion of information in transient social networks based on the research results of information diffusion in online social networks has become the research direction. In this paper, the characteristics and research status of transient social networks are analyzed, and it is pointed out that the existing research results can not fully consider the characteristics of social networks. Then, the development of information diffusion and information diffusion maximization is introduced and analyzed in detail. In order to solve the problem of information diffusion in transient social networks, a structural hole based information diffusion model is proposed. Firstly, the existing methods are usually aimed at online social networks and can not be directly applied to transient social networks. Secondly, combining the information diffusion model in the online social network, whether there is a model that can satisfy the characteristics of the transient social network. Finally, based on the real data set, the model is verified by experiments. The variability of transient social networks leads to a very short time of existence. If it is too narrow to study transient social networks at any given time, we will combine the structural holes. By integrating transient social networks at all times to form global transient social networks, we can study them like online social networks. Therefore, as with any other network, as long as we can find the characteristics of the network, we can improve our model to adapt to the transient social network. Then we continue to study the maximization of information diffusion and analyze the characteristics of encounter time and encounter times in transient networks. Firstly, the concept of transient social network is proposed and the definition of the problem is given. Then, the influence of nodes is determined based on the number of encounters and the time of encounters. In the global transient social network, the set of the most influential nodes is found and used as the initial audience. It effectively solves the problem of information diffusion maximization in transient social networks. Finally, experiments are carried out on two real data sets to verify the feasibility and effectiveness of the proposed algorithm.
【學位授予單位】：江西財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.09

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本文編號：2202487