本文關(guān)鍵詞： 移動社交網(wǎng)絡(luò) 相依網(wǎng)絡(luò) 魯棒性 社區(qū)發(fā)現(xiàn)　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：本文探討了移動社交網(wǎng)絡(luò)的結(jié)構(gòu)特征和網(wǎng)絡(luò)特性,基于復(fù)雜網(wǎng)絡(luò)理論提出了一種基于組增長的無標(biāo)度網(wǎng)絡(luò)模型,根據(jù)移動社交網(wǎng)絡(luò)中用戶和設(shè)備之間不同的依賴支持關(guān)系構(gòu)建了兩種不同網(wǎng)間關(guān)系的相依網(wǎng)絡(luò),一種是描述移動社交網(wǎng)絡(luò)一對一相互依賴關(guān)系的相依網(wǎng)絡(luò),另一種是描述移動社交網(wǎng)絡(luò)多重依賴支持關(guān)系的相依網(wǎng)絡(luò)。文中將攻擊策略分為隨機(jī)攻擊和蓄意攻擊度值大的節(jié)點(diǎn),分別討論了在兩種攻擊策略下不同相依關(guān)系的移動社交網(wǎng)絡(luò)的結(jié)構(gòu)變化,并且分析了它們的魯棒性。通過理論推導(dǎo)得到在隨機(jī)攻擊下,不同網(wǎng)絡(luò)模型發(fā)生大規(guī)模失效現(xiàn)象的閾值,然后進(jìn)行計(jì)算機(jī)仿真模擬相繼故障發(fā)生的過程,在去除一定比例節(jié)點(diǎn)之后,網(wǎng)絡(luò)節(jié)點(diǎn)發(fā)生相繼失效,最終剩余的未失效的節(jié)點(diǎn)組成了剩余最大聚簇,剩余最大聚簇的大小代表了網(wǎng)絡(luò)魯棒性的強(qiáng)弱。當(dāng)去除節(jié)點(diǎn)比例達(dá)到閾值時,網(wǎng)絡(luò)發(fā)生“雪崩”現(xiàn)象,即不存在剩余最大聚簇。研究結(jié)果表明,在隨機(jī)攻擊下,具有相依關(guān)系的移動社交網(wǎng)絡(luò)模型比單個網(wǎng)絡(luò)模型的魯棒性弱;在蓄意攻擊下,具有相依關(guān)系的移動社交網(wǎng)絡(luò)模型比單個網(wǎng)絡(luò)模型的魯棒性強(qiáng)。然而,無論是在隨機(jī)攻擊還是蓄意攻擊下,具有一對一相依關(guān)系的移動社交網(wǎng)絡(luò)和具有多重依賴支持關(guān)系的移動社交網(wǎng)絡(luò)相比魯棒性都要弱。根據(jù)真實(shí)社交網(wǎng)絡(luò)的動態(tài)性,本文提出了一種自適應(yīng)社區(qū)發(fā)現(xiàn)算法,與傳統(tǒng)的靜態(tài)社區(qū)發(fā)現(xiàn)算法不同的是該算法引入了自適應(yīng)的概念,不需要考慮當(dāng)前網(wǎng)絡(luò)全部拓?fù)浣Y(jié)構(gòu),只需通過之前的網(wǎng)絡(luò)社區(qū)劃分和網(wǎng)絡(luò)結(jié)構(gòu)的變化就能劃分出新的社區(qū)結(jié)構(gòu)。該算法可以在動態(tài)網(wǎng)絡(luò)中進(jìn)行社區(qū)劃分,本文通過在真實(shí)數(shù)據(jù)集和人工合成數(shù)據(jù)集上的實(shí)驗(yàn)分析該算法的準(zhǔn)確性。實(shí)驗(yàn)結(jié)果表明,該算法在NMI評價標(biāo)準(zhǔn)下,與其他算法相比具有較好的表現(xiàn)。文章的最后我們對研究工作做出了總結(jié),分析研究中存在的不足之處,提出未來的研究展望。
[Abstract]:In this paper, the structure and network characteristics of mobile social networks are discussed, and a scale-free network model based on group growth is proposed based on complex network theory. According to the different dependency support relationships between users and devices in mobile social networks, two kinds of dependent networks with different network relationships are constructed, one is the dependent networks that describe the one-to-one interdependence of mobile social networks. The other is a dependent network that describes the multi-dependency support relationship of mobile social networks. In this paper, the attack strategy is divided into nodes with high degree of random attack and deliberate attack. The structural changes of mobile social networks with different dependencies under two attack strategies are discussed, and their robustness is analyzed. The threshold of large-scale failure occurs in different network models, and then computer simulation is carried out to simulate the process of successive failures. After removing a certain proportion of nodes, the network nodes fail successively. The residual nodes formed the largest residual cluster, and the residual maximum cluster size represented the robustness of the network. When the proportion of nodes removed reached the threshold, the "avalanche" phenomenon occurred in the network. The results show that under random attack, the robustness of the dependent mobile social network model is weaker than that of the single network model, and the robustness of the mobile social network model is weaker than that of the single network model under the deliberate attack. The model of mobile social network with dependency is more robust than the model of single network. However, whether in random attack or deliberate attack, The robustness of mobile social networks with one-to-one dependencies and multi-dependency support relationships is weaker than that of mobile social networks. According to the dynamic nature of real social networks, an adaptive community discovery algorithm is proposed in this paper. Different from the traditional static community discovery algorithm, this algorithm introduces the concept of adaptive, and does not need to consider all the current network topology. The new community structure can be divided by the former network community division and the network structure change. This algorithm can divide the community in the dynamic network. In this paper, the accuracy of the algorithm is analyzed by experiments on real data sets and synthetic data sets. The experimental results show that the algorithm is based on the NMI evaluation standard. At the end of the paper, we summarize the research work, analyze the shortcomings of the research, and put forward the future research prospects.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.01;O157.5

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