【摘要】：在社會(huì)網(wǎng)絡(luò)中,各種謠言不斷傳播,對(duì)國(guó)家和社會(huì)的穩(wěn)定造成極大的威脅,有效地定位信息傳播源點(diǎn)對(duì)于預(yù)測(cè)傳播范圍、控制傳播過(guò)程等具有重要的意義。社會(huì)網(wǎng)絡(luò)的最主要的特性是動(dòng)態(tài)特性,即隨著時(shí)間的推進(jìn),社會(huì)網(wǎng)絡(luò)中的節(jié)點(diǎn)可能增加或減少,其中的邊也會(huì)增加或減少。因此,能夠通過(guò)建立社會(huì)網(wǎng)絡(luò)的演化模型很好地模擬社會(huì)網(wǎng)絡(luò)的演化規(guī)律對(duì)于定位信息源點(diǎn)至關(guān)重要。本文進(jìn)行的研究主要以兩大前提條件為基礎(chǔ)：一是假設(shè)社會(huì)網(wǎng)絡(luò)的演化只是從邊的增加角度進(jìn)行；二是假設(shè)已知當(dāng)前的傳播拓?fù)浜投ㄎ粫r(shí)間與真正傳播拓?fù)湫纬蓵r(shí)間的差值。本文與之前源點(diǎn)定位算法的最大不同是,考慮到社會(huì)網(wǎng)絡(luò)的動(dòng)態(tài)演化,從而在定位性能上較擴(kuò)展的基于觀察點(diǎn)的單源點(diǎn)定位算法有相對(duì)的提高。本文從社會(huì)網(wǎng)絡(luò)的動(dòng)態(tài)特性出發(fā),考慮到在信息源點(diǎn)定位的情形中,源點(diǎn)定位時(shí)的網(wǎng)絡(luò)拓?fù)渑c真正的傳播拓?fù)洳煌?應(yīng)用EPSO模型對(duì)社會(huì)網(wǎng)絡(luò)進(jìn)行建模,同時(shí)采用雙曲映射算法預(yù)測(cè)社會(huì)網(wǎng)絡(luò)中的鏈接。應(yīng)用雙曲映射算法,根據(jù)當(dāng)前傳播拓?fù)漕A(yù)測(cè)新生成的鏈接,將預(yù)測(cè)出的鏈接從當(dāng)前傳播拓?fù)渲袆h除得到估計(jì)出的真正的傳播拓?fù)?以這個(gè)拓?fù)錇榛A(chǔ)使用單源點(diǎn)定位算法預(yù)測(cè)信息傳播源點(diǎn)。本文采用雙曲映射算法在合成網(wǎng)絡(luò)和實(shí)際網(wǎng)絡(luò)上預(yù)測(cè)將來(lái)的鏈接,其傳播模型是隨機(jī)傳播模型,這個(gè)模型是SI模型。在定位時(shí)進(jìn)行了各種對(duì)比實(shí)驗(yàn),比如在同一個(gè)傳播拓?fù)湎?不同的觀察點(diǎn)部署策略；在同一個(gè)傳播拓?fù)湎?不同觀察點(diǎn)部署比例。從實(shí)驗(yàn)結(jié)果來(lái)看,我們的算法的定位性能總體上優(yōu)于擴(kuò)展的基于觀察點(diǎn)的單源點(diǎn)定位算法。由此,可以推斷出本文提出的定位算法在社會(huì)網(wǎng)絡(luò)上的信息傳播源點(diǎn)定位中效果明顯,它對(duì)于社會(huì)網(wǎng)絡(luò)上的謠言定位和控制有重大作用。
[Abstract]:In the social network, the spread of various rumors poses a great threat to the stability of the country and society. It is of great significance to locate the source of information effectively for predicting the spread range and controlling the communication process. The most important characteristic of social network is dynamic characteristic, that is, the nodes in social network may increase or decrease, and the side of social network will increase or decrease. Therefore, it is very important to simulate the evolution law of social network by establishing the evolution model of social network for locating information source points. The research in this paper is mainly based on two prerequisites: one is to assume that the evolution of social networks is only from the angle of the increase of edges; the other is to assume the difference between the known current propagation topology and the time of localization and the time when the real propagation topology is formed. The biggest difference between this paper and the previous source point localization algorithm is that considering the dynamic evolution of social network, the single source point location algorithm based on observation point is relatively improved in the performance of localization. Based on the dynamic characteristics of social network and considering that the network topology of source point location is different from the real transmission topology, the EPSO model is used to model the social network. At the same time, hyperbolic mapping algorithm is used to predict the links in social networks. The hyperbolic mapping algorithm is applied to predict the newly generated links according to the current propagating topology, and the estimated true propagation topology is obtained by removing the predicted links from the current propagating topology. Based on this topology, a single source location algorithm is used to predict the source points of information propagation. In this paper, hyperbolic mapping algorithm is used to predict the future links on the synthetic network and the real network. The propagation model of hyperbolic mapping algorithm is a random propagation model, and this model is a SI model. A variety of comparative experiments are carried out, such as the deployment strategies of different observation points under the same propagation topology, and the different deployment ratios of the observation points under the same propagation topology. The experimental results show that the performance of our algorithm is better than that of the extended single source location algorithm based on observation point. Therefore, it can be inferred that the localization algorithm proposed in this paper is effective in locating the sources of information spread on social networks, and it plays an important role in the localization and control of rumors on social networks.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09;G206

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