基于雙曲映射算法的社會網(wǎng)絡演化建模及傳播源點定位方法研究
發(fā)布時間：2018-11-28 15:01
【摘要】：在社會網(wǎng)絡中,各種謠言不斷傳播,對國家和社會的穩(wěn)定造成極大的威脅,有效地定位信息傳播源點對于預測傳播范圍、控制傳播過程等具有重要的意義。社會網(wǎng)絡的最主要的特性是動態(tài)特性,即隨著時間的推進,社會網(wǎng)絡中的節(jié)點可能增加或減少,其中的邊也會增加或減少。因此,能夠通過建立社會網(wǎng)絡的演化模型很好地模擬社會網(wǎng)絡的演化規(guī)律對于定位信息源點至關重要。本文進行的研究主要以兩大前提條件為基礎：一是假設社會網(wǎng)絡的演化只是從邊的增加角度進行；二是假設已知當前的傳播拓撲和定位時間與真正傳播拓撲形成時間的差值。本文與之前源點定位算法的最大不同是,考慮到社會網(wǎng)絡的動態(tài)演化,從而在定位性能上較擴展的基于觀察點的單源點定位算法有相對的提高。本文從社會網(wǎng)絡的動態(tài)特性出發(fā),考慮到在信息源點定位的情形中,源點定位時的網(wǎng)絡拓撲與真正的傳播拓撲不同,應用EPSO模型對社會網(wǎng)絡進行建模,同時采用雙曲映射算法預測社會網(wǎng)絡中的鏈接。應用雙曲映射算法,根據(jù)當前傳播拓撲預測新生成的鏈接,將預測出的鏈接從當前傳播拓撲中刪除得到估計出的真正的傳播拓撲,以這個拓撲為基礎使用單源點定位算法預測信息傳播源點。本文采用雙曲映射算法在合成網(wǎng)絡和實際網(wǎng)絡上預測將來的鏈接,其傳播模型是隨機傳播模型,這個模型是SI模型。在定位時進行了各種對比實驗,比如在同一個傳播拓撲下,不同的觀察點部署策略；在同一個傳播拓撲下,不同觀察點部署比例。從實驗結(jié)果來看,我們的算法的定位性能總體上優(yōu)于擴展的基于觀察點的單源點定位算法。由此,可以推斷出本文提出的定位算法在社會網(wǎng)絡上的信息傳播源點定位中效果明顯,它對于社會網(wǎng)絡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.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09;G206
[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.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09;G206
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