【摘要】：社會(huì)網(wǎng)絡(luò)是描述具有社會(huì)屬性的個(gè)體及其相互間關(guān)系的一類網(wǎng)絡(luò),社會(huì)網(wǎng)絡(luò)中信息擴(kuò)散的研究已成熱門領(lǐng)域,如輿情控制,可建立信息擴(kuò)散模型,揭示其傳播的特征及規(guī)律,再采用科學(xué)方法來預(yù)防和處理輿情的泛濫,由此可見,研究社會(huì)網(wǎng)絡(luò)對(duì)真實(shí)社會(huì)網(wǎng)絡(luò)有極為重要意義。隨著網(wǎng)絡(luò)信息爆炸式增長(zhǎng),社會(huì)網(wǎng)絡(luò)中個(gè)體之間的關(guān)系不再是單一的,因此,近年來有研究者提出了一種新型的社會(huì)網(wǎng)絡(luò)——多層社會(huì)網(wǎng)絡(luò)(Multi-layered Social Network, MSN),即把復(fù)雜關(guān)系抽象成每層僅存一種社會(huì)關(guān)系的分層結(jié)構(gòu)的社會(huì)網(wǎng)絡(luò),從而將平面網(wǎng)絡(luò)關(guān)系圖轉(zhuǎn)換為立體網(wǎng)絡(luò)結(jié)構(gòu),能夠更好地刻畫真實(shí)生活中社會(huì)網(wǎng)絡(luò)形態(tài)與特征。 信息擴(kuò)散過程中,首先將多層社會(huì)網(wǎng)絡(luò)進(jìn)行社團(tuán)結(jié)構(gòu)劃分,其次建立多層社會(huì)網(wǎng)絡(luò)的傳播模型,最后采用影響最大化策略來實(shí)現(xiàn)信息擴(kuò)散的最大化目標(biāo)。多層社會(huì)網(wǎng)絡(luò)是社會(huì)網(wǎng)絡(luò)中最新的研究領(lǐng)域之一,社會(huì)網(wǎng)絡(luò)中現(xiàn)存的很多算法和模型都不適用于多層社會(huì)網(wǎng)絡(luò),或者算法較粗糙等,因此,迫切需要解決這些關(guān)鍵問題。 本文針對(duì)多層社會(huì)網(wǎng)絡(luò)信息擴(kuò)散進(jìn)行了如下研究： (1)多層社會(huì)網(wǎng)絡(luò)的社團(tuán)發(fā)現(xiàn)算法：社團(tuán)是構(gòu)成整個(gè)MSN的子集,其主要特征是每個(gè)社團(tuán)內(nèi)部的節(jié)點(diǎn)間的有著相對(duì)緊湊的連接方式,而各個(gè)社團(tuán)之間卻只存在著相對(duì)比較稀疏的連接方式。目前社團(tuán)發(fā)現(xiàn)算法主要集中在單層的社會(huì)網(wǎng)絡(luò),而多層社會(huì)網(wǎng)絡(luò)MSN的社團(tuán)發(fā)現(xiàn)算法較少,社團(tuán)劃分結(jié)果較粗糙等特點(diǎn),為了既考慮多層社會(huì)關(guān)系又區(qū)分對(duì)待不同層數(shù)的情況,以及考慮節(jié)點(diǎn)間本身的連接強(qiáng)度問題,本文提出了一種基于邊聚類的多層社會(huì)網(wǎng)絡(luò)社團(tuán)發(fā)現(xiàn)(CLEDCC)算法。該算法充分考慮了層數(shù)給真實(shí)社會(huì)網(wǎng)絡(luò)帶來的現(xiàn)實(shí)意義,并對(duì)CLEDCC算法的數(shù)學(xué)模型和算法流程進(jìn)行詳細(xì)說明,仿真實(shí)驗(yàn)和分析,并與相關(guān)算法進(jìn)行比較,該算法無需調(diào)整參數(shù),算法穩(wěn)定性高,劃分結(jié)果精準(zhǔn)。 (2)多層社會(huì)網(wǎng)絡(luò)的信息傳播模型：在單層社會(huì)網(wǎng)絡(luò)中提出的信息傳播模型,研究非常多,但是不太實(shí)用于多層社會(huì)網(wǎng)絡(luò),而目前這方面幾乎沒有相關(guān)研究。對(duì)于多層社會(huì)網(wǎng)絡(luò)的處理一般都是通過模型簡(jiǎn)化處理,將多層復(fù)雜關(guān)系簡(jiǎn)化為單層社會(huì)網(wǎng)絡(luò),而單層社會(huì)網(wǎng)絡(luò)的傳播模型,對(duì)于問題的處理不夠精細(xì),故本文構(gòu)建一種新型的多層電阻器傳播(CRM)模型。詳細(xì)重點(diǎn)地介紹該模型的理論基礎(chǔ),并由物理學(xué)電路模型的思想演變而來,通過圖論、概率論等大量的數(shù)學(xué)理論做支撐,證明了模型的可行性。CRM傳播模型有著非常好的特點(diǎn),與真實(shí)網(wǎng)絡(luò)的形態(tài)非常接近,CRM模型是非常適用于多層社會(huì)網(wǎng)絡(luò)。 (3)信息擴(kuò)散：給一個(gè)網(wǎng)絡(luò)(V,E),在一定時(shí)間內(nèi),尋找盡可能少的種子節(jié)點(diǎn),盡可能多的影響網(wǎng)絡(luò)中的其他節(jié)點(diǎn),這是一個(gè)NP-hard問題。采用本文提出的CLEDCC算法來實(shí)現(xiàn)更好的社團(tuán)結(jié)構(gòu),其次利用CRM模型來仿真社會(huì)網(wǎng)絡(luò)信息擴(kuò)散過程,再擴(kuò)展現(xiàn)有的社團(tuán)與度啟發(fā)CDH策略,通過CDH-CLEDCC來實(shí)現(xiàn)信息擴(kuò)散最大化。
[Abstract]:Social network is a kind of network describing individuals with social attributes and their relationships. The study of information diffusion in social network has become a hot field. For example, public opinion control, information diffusion model can be established to reveal the characteristics and laws of its propagation, and then scientific methods can be used to prevent and deal with the overflow of public opinion. Thus, the study of society can be seen. With the explosive growth of network information, the relationship between individuals in social networks is no longer single. Therefore, in recent years, researchers have proposed a new type of social network, multi-layered social network (MSN), which abstracts complex relationships into each layer only. A social network with hierarchical structure of social relations is preserved, thus transforming the plane network diagram into a three-dimensional network structure, which can better describe the form and characteristics of social networks in real life.
In the process of information diffusion, the multi-layer social network is divided into several groups, and then the propagation model of multi-layer social network is established. Finally, the maximization of information diffusion is achieved by using the strategy of influence maximization. T-type is not suitable for multi-layer social networks, or the algorithm is rough, so it is urgent to solve these key problems.
In this paper, the information diffusion of multi-layer social networks is studied as follows:
(1) Club discovery algorithm in multi-layer social networks: Club is a subset of the whole MSN, and its main feature is that there is a relatively compact connection between nodes within each community, while there is only a relatively sparse connection between the various communities. Multi-layer social network MSN has fewer community discovery algorithms and rough community partitioning results. In order to consider the multi-layer social relations and different layers, and consider the connection strength between nodes, this paper proposes a multi-layer social network community discovery algorithm based on edge clustering (CLEDCC). Considering the practical significance of the real social network brought by the number of layers, the mathematical model and algorithm flow of CLEDCC algorithm are described in detail, and the simulation experiment and analysis are carried out. Compared with the related algorithms, the algorithm does not need to adjust parameters, and has high stability and accurate partition results.
(2) Information dissemination model of multi-layer social network: The information dissemination model proposed in single-layer social network has been studied very much, but it is not very practical for multi-layer social network, and there is little research in this area at present. A new multilayer resistor propagation (CRM) model is constructed in this paper. The theoretical basis of the model is introduced in detail, which is derived from the idea of physical circuit model and supported by a large number of mathematical theories such as graph theory and probability theory. The CRM communication model has very good characteristics and is very close to the real network. The CRM model is very suitable for multi-layer social networks.
(3) Information diffusion: For a network (V, E), it is a NP-hard problem to find as few seed nodes as possible within a certain period of time and to influence as many other nodes as possible in the network. The CLEDCC algorithm proposed in this paper is used to achieve a better community structure. Secondly, the CRM model is used to simulate the process of information diffusion in the social network, and then extended. The existing community and degree heuristic CDH strategy can maximize information diffusion through CDH-CLEDCC.
【學(xué)位授予單位】：陜西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.0

【共引文獻(xiàn)】

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2 張莉莉;組織文化對(duì)于組織成員作用機(jī)制研究：借鑒催化動(dòng)力學(xué)方法[D];北京交通大學(xué);2012年

3 鄭向杰;基于企業(yè)嵌入視角的聯(lián)盟創(chuàng)新網(wǎng)絡(luò)中知識(shí)共享與創(chuàng)新研究[D];上海大學(xué);2014年

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本文編號(hào)：2238376