本文關(guān)鍵詞：在線社交網(wǎng)絡(luò)模型演化及傳播機(jī)制研究　出處：《山東師范大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 社交網(wǎng)絡(luò) 網(wǎng)絡(luò)演化 信息傳播模型 節(jié)點(diǎn)影響力

【摘要】：隨著全球互聯(lián)網(wǎng)技術(shù)的發(fā)展,在線社交網(wǎng)絡(luò)已經(jīng)取代了電視、報(bào)紙等傳統(tǒng)媒體,成為主流信息傳播平臺(tái)。信息傳播方式的改變,使人們在快捷獲取、傳播信息的同時(shí),也受到了信息爆炸、虛假謠言等問題的干擾。因此,研究并發(fā)現(xiàn)信息在社交網(wǎng)絡(luò)的傳播規(guī)律,對(duì)互聯(lián)網(wǎng)的信息管理、輿情管控、網(wǎng)絡(luò)事件的實(shí)時(shí)跟蹤及預(yù)警等具有重要作用,對(duì)維護(hù)國家安全及社會(huì)穩(wěn)定也具有十分重要的現(xiàn)實(shí)意義�；诖�,本文針對(duì)社交網(wǎng)絡(luò)的演化機(jī)制及信息傳播的相關(guān)理論展開研究,主要內(nèi)容如下:(1)在BA無標(biāo)度網(wǎng)絡(luò)模型基礎(chǔ)上,建立基于邊數(shù)隨機(jī)增長的網(wǎng)絡(luò)演化模型,嘗試揭示真實(shí)網(wǎng)絡(luò)度分布在雙對(duì)數(shù)坐標(biāo)下頭部彎曲現(xiàn)象的機(jī)理。網(wǎng)絡(luò)演化過程中,節(jié)點(diǎn)在加入社交網(wǎng)絡(luò)時(shí)選擇朋友的數(shù)目具有隨機(jī)性,本文根據(jù)邊數(shù)增長數(shù)量完全服從泊松分布、部分服從泊松分布和選擇概率完全隨機(jī)三種情況,建立了三個(gè)網(wǎng)絡(luò)演化模型,并推導(dǎo)出三個(gè)模型的度分布函數(shù)。研究表明,在保持擇優(yōu)概率的機(jī)制下,邊數(shù)增長的隨機(jī)性可以導(dǎo)致度分布出現(xiàn)彎曲現(xiàn)象;當(dāng)節(jié)點(diǎn)度較大時(shí),度分布仍然服從冪律分布;如果將擇優(yōu)概率變成隨機(jī)概率,則會(huì)破壞度分布的冪律分布。根據(jù)模型算法,本文構(gòu)建了相應(yīng)的仿真網(wǎng)絡(luò)模型,實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證了模型假設(shè)的合理性和理論分析的正確性。(2)根據(jù)在線社交網(wǎng)絡(luò)信息傳播特點(diǎn)和目前社交網(wǎng)絡(luò)傳播模型研究中存在的問題,提出一種基于用戶相對(duì)權(quán)重的信息傳播模型——RWSIR模型。社交網(wǎng)絡(luò)中,信息能否順利傳播,很大程度上取決于傳播者和接收者雙方的地位關(guān)系,本文定義了社交網(wǎng)絡(luò)用戶間的相互影響力函數(shù),并對(duì)網(wǎng)絡(luò)中的傳播路徑及傳播過程進(jìn)行了分析,討論了不同路徑的傳播影響力,提出了一種基于相對(duì)權(quán)重的信息傳播模型,給出了傳播動(dòng)力學(xué)方程。為進(jìn)一步驗(yàn)證模型的有效性,本文在不同的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)下,分別以權(quán)威節(jié)點(diǎn)和普通節(jié)點(diǎn)作為傳播節(jié)點(diǎn),將傳統(tǒng)的SIR模型和本文模型進(jìn)行了仿真實(shí)驗(yàn)。實(shí)驗(yàn)數(shù)據(jù)表明,普通節(jié)點(diǎn)在傳播過程中一般會(huì)弱于權(quán)威節(jié)點(diǎn),而在某些特殊條件下差別不大;兩類模型在均勻網(wǎng)絡(luò)中沒有明顯差異,但在非均勻網(wǎng)絡(luò)中存在明顯差異,本文模型更能體現(xiàn)真實(shí)網(wǎng)絡(luò)中信息傳播的特點(diǎn)。(3)根據(jù)謠言在社交網(wǎng)絡(luò)中的傳播特點(diǎn),基于SEIR傳染病模型提出了一種改進(jìn)的謠言傳播模型謠言傳播不同于人類的傳染病傳播,民眾聽到謠言后會(huì)首先辨別其真?zhèn)涡?然后決定是否傳播。本文根據(jù)民眾對(duì)謠言的掌握情況將整個(gè)網(wǎng)絡(luò)中的人群分為四類:未知消息者、傳播者、知情者、不感興趣者。為詳細(xì)刻畫民眾接收謠言后的不同反應(yīng),引入了四類人群的不同轉(zhuǎn)化概率,建立了謠言傳播模型動(dòng)力學(xué)方程,并證明了方程解的穩(wěn)定性,分析了不同轉(zhuǎn)化概率對(duì)方程解的影響。最后在不同的網(wǎng)絡(luò)拓?fù)渖线M(jìn)行了仿真實(shí)驗(yàn),實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證了理論的正確性。(4)針對(duì)靜態(tài)網(wǎng)絡(luò)節(jié)點(diǎn)影響力排序算法的局限性,提出一種基于傳播概率的節(jié)點(diǎn)影響力算法——PIC算法現(xiàn)有研究表明,網(wǎng)絡(luò)中節(jié)點(diǎn)的影響力隨著傳播概率的不同而發(fā)生變化,基本節(jié)點(diǎn)影響力排序算法不能很好的解決上述問題。據(jù)此,本文在分析傳播節(jié)點(diǎn)對(duì)網(wǎng)絡(luò)中不同層鄰居節(jié)點(diǎn)的影響力基礎(chǔ)上,考慮傳播概率對(duì)信息傳播的影響,構(gòu)建了一種既考慮網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),又體現(xiàn)傳播概率的節(jié)點(diǎn)影響力算法,并進(jìn)一步給出了近似計(jì)算方法。本文利用基本算法和PIC算法計(jì)算了不同網(wǎng)絡(luò)的節(jié)點(diǎn)排序,數(shù)據(jù)結(jié)果顯示,本文提出的算法綜合穩(wěn)定性和效率要優(yōu)于其余基本算法。
[Abstract]:With the development of Internet technology, online social network has replaced the television, newspapers and other traditional media, has become the mainstream of information dissemination platform. Changes in the way of information dissemination, so that people in the fast access, dissemination of information at the same time, is also affected by the explosion of information, false rumors and other issues. Therefore, to study and find out the propagation law of the information in the social network, information management, the Internet public opinion monitoring, network event tracking and warning plays an important role, but also has very important practical significance to safeguard national security and social stability. Based on the relevant theories of the evolution mechanism for social networks and information dissemination, main content are as follows: (1) the scale-free network model based on BA, the establishment of evolution model of randomly growing edges based on the network, try to reveal the true network degree distribution in double logarithmic coordinates The mechanism of head bending phenomenon. The network evolution process, node selection in the number of friends to join the social network with random, according to the number of edges of the growth in the number of completely obeys the Poisson distribution, the Poisson distribution and the probability of choosing some completely random three cases, established the evolution model of three networks, and deduces the three models the degree distribution function. The results show that the preferred mechanism to keep the probability, random number of edges growth can lead to the degree distribution of bending phenomenon; when the node degree is large, the degree distributions follow the power-law distribution; if the preferred probability into random probability, it will destroy the power-law degree distribution. According to the model algorithm in this paper, builds the corresponding network model, the experimental data to verify the correctness of the model assumptions and theoretical analysis. (2) according to the online social network information dissemination characteristics and the current society To research the problems in network communication model, put forward a kind of information communication model based on the user's relative weight RWSIR model. The social network, smooth information communication, depends largely on the status of both the disseminator and the receiver, this paper defines the mutual influence function of the social network between users, and to spread the path and propagation process in the network are analyzed, discussed the different path of the spread of influence, put forward a kind of information dissemination model based on relative weight, transmission dynamics equations are given. For further validation, based on different network topologies, with authority nodes and ordinary nodes respectively as communication node, the SIR model and the traditional model of the simulation experiments. The experimental data show that ordinary nodes in the communication process generally weaker than the authority of the node, and In some special conditions no difference; there is no significant difference in the homogeneous network two models, but there were significant differences in the heterogeneous network, this model can better reflect the characteristics of the information spreading in real networks. (3) according to the propagation characteristics of rumors in a social network, based on the SEIR epidemic model is proposed a rumor propagation model for rumor improved is different from the spread of infectious diseases of mankind, people after hearing the rumors will be the first to identify its authenticity, and then decide whether to spread. According to the people to grasp the rumors in the network group is divided into four categories: the unknown message, communicator, not feeling interested. For different reaction to describe people receive rumors after the different conversion probability introduced four kinds of people, to establish the dynamic equation of rumor propagation model, and prove the stability of the solution of equations, analysis of different Influence of probability of the integral equation. Finally, simulation experiments are carried out in different network topologies, the experimental data to verify the correctness of the theory. (4) aiming at the limitations of static network node influence sorting algorithm, proposed based on node transmission probability influence algorithm - PIC algorithm of existing research shows that the nodes in the network with the influence of propagation probability changes, the basic node influence ranking algorithm can't solve the problem. Therefore, based on the analysis of influence of different communication nodes based on the network layer of the neighbor nodes, considering the influence of the propagation probability of information dissemination, constructs a both network topology, node influence the algorithm shows the propagation probability, and further gives the approximate calculation method. This paper uses the basic algorithm and PIC algorithm in different network node scheduling, The results show that the integrated stability and efficiency of the proposed algorithm are better than those of the other basic algorithms.

【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09;G206

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