本文選題：復(fù)雜網(wǎng)絡(luò) + 技術(shù)交易網(wǎng)絡(luò)　； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：國(guó)內(nèi)外學(xué)者已經(jīng)成功的將復(fù)雜性科學(xué)的一些思想應(yīng)用于刻畫(huà)和解釋技術(shù)交易網(wǎng)絡(luò),但是這些嘗試主要停留在宏觀層面,針對(duì)技術(shù)交易網(wǎng)絡(luò)中觀及微觀層面的定性定量研究還少見(jiàn)報(bào)道。相比而言,通過(guò)對(duì)網(wǎng)絡(luò)中社團(tuán)結(jié)構(gòu)的深入細(xì)致分析,可以更全面深刻的掌握系統(tǒng)結(jié)構(gòu)本身存在的規(guī)律,有利于構(gòu)建良好的技術(shù)交易模式,促進(jìn)技術(shù)交易活動(dòng)的順利進(jìn)行。本文將復(fù)雜網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)檢測(cè)算法應(yīng)用到技術(shù)交易行為的研究過(guò)程當(dāng)中,以實(shí)際交易數(shù)據(jù)擬合網(wǎng)絡(luò)生成模型,通過(guò)技術(shù)交易網(wǎng)絡(luò)對(duì)社團(tuán)檢測(cè)算法進(jìn)行實(shí)證與分析。主要展開(kāi)以下方面的具體工作:(1)技術(shù)交易網(wǎng)絡(luò)模型的構(gòu)建。在構(gòu)建出復(fù)雜技術(shù)交易網(wǎng)絡(luò)之后,提取網(wǎng)絡(luò)靜態(tài)特性及動(dòng)態(tài)特性,對(duì)網(wǎng)絡(luò)結(jié)構(gòu)進(jìn)行初步理解。(2)分析多種社團(tuán)檢測(cè)方法并進(jìn)行實(shí)證。分別對(duì)層次聚類算法、模塊度優(yōu)化算法、譜聚類算法以及流傳播算法四類社團(tuán)檢測(cè)算法進(jìn)行分析。通過(guò)對(duì)比模塊度、時(shí)間復(fù)雜度等社團(tuán)劃分評(píng)價(jià)標(biāo)準(zhǔn)發(fā)現(xiàn):模塊度優(yōu)化類算法對(duì)于技術(shù)交易網(wǎng)絡(luò)的切合程度較高。(3)深入分析技術(shù)交易網(wǎng)絡(luò)中重要節(jié)點(diǎn)及典型社團(tuán),發(fā)掘網(wǎng)絡(luò)內(nèi)部結(jié)構(gòu)規(guī)律。首先,對(duì)多種中心性進(jìn)行加權(quán)獲得新的中心性評(píng)估標(biāo)準(zhǔn);其次,通過(guò)重要節(jié)點(diǎn)所在社團(tuán)的演化研究發(fā)現(xiàn):社團(tuán)內(nèi)部主要通過(guò)重要節(jié)點(diǎn)吸引外部節(jié)點(diǎn)融入到社團(tuán)當(dāng)中。通過(guò)對(duì)網(wǎng)絡(luò)內(nèi)部社團(tuán)聚類后的結(jié)果發(fā)現(xiàn),節(jié)點(diǎn)度大的社團(tuán)向外分枝較多,節(jié)點(diǎn)度小的社團(tuán)內(nèi)聯(lián)系較為緊密。本文通過(guò)研究與實(shí)證相結(jié)合,以北京市技術(shù)交易網(wǎng)絡(luò)為基礎(chǔ)進(jìn)行社團(tuán)劃分,發(fā)現(xiàn)使用模塊度優(yōu)化類算法在時(shí)間復(fù)雜度及劃分結(jié)果等方面均具有較好表現(xiàn),且通過(guò)算法發(fā)現(xiàn)的社團(tuán)內(nèi)部結(jié)構(gòu)規(guī)律能夠在一定程度上為對(duì)技術(shù)交易市場(chǎng)的管理提供指導(dǎo)與支持。
[Abstract]:Scholars at home and abroad have successfully applied some ideas of complexity science to the description and interpretation of technology trading networks, but these attempts are mainly at the macro level. Qualitative and quantitative studies on the meso-and micro-level of technology trading networks are rarely reported. In contrast, through the thorough and careful analysis of the community structure in the network, we can grasp the rules of the system structure more comprehensively and profoundly, which is conducive to the construction of a good technology trading model and the smooth progress of the technology trading activities. In this paper, the complex network community structure detection algorithm is applied to the research process of technical transaction behavior. The network generation model is fitted with the actual transaction data, and the community detection algorithm is empirically analyzed through the technology transaction network. The main work of the following aspects of the specific work: 1) the construction of technology trading network model. After the complex technical transaction network is constructed, the static and dynamic characteristics of the network are extracted, and the network structure is preliminarily understood. The hierarchical clustering algorithm, modular optimization algorithm, spectral clustering algorithm and flow propagation algorithm are analyzed respectively. By comparing the evaluation criteria of community division, such as modularity and time complexity, it is found that the modularity optimization algorithm has a higher degree of relevance to the technical trading network. The important nodes and typical communities in the technology trading network are analyzed in depth. Explore the internal structure of the network law. Firstly, new evaluation criteria of centrality are obtained by weighting various kinds of centrality. Secondly, through the study of the evolution of the community in which the important nodes are located, it is found that the internal community mainly attracts the external nodes into the community through the important nodes. The results show that the communities with large nodes are more branched out and the communities with small nodes are more closely connected. Through the combination of research and demonstration, this paper divides communities based on Beijing technology trading network, and finds that the modular degree optimization algorithm has better performance in terms of time complexity and partition results. And the internal structure of the community found by the algorithm can provide guidance and support for the management of the technology trading market to a certain extent.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5

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