【摘要】：復(fù)雜網(wǎng)絡(luò)是代表和分析復(fù)雜系統(tǒng)(例如萬(wàn)維網(wǎng)和交通運(yùn)輸系統(tǒng))的一種有用的工具。但是,隨著復(fù)雜網(wǎng)絡(luò)體量的增長(zhǎng),理解網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)和它們的特征變得越來(lái)越困難。 在本文中,我們首先回顧了8種用于檢測(cè)并且可以排序網(wǎng)絡(luò)中關(guān)鍵邊的方法,并分析這些方法的適用性與局限性。接著,我們將這些方法應(yīng)用于4個(gè)真實(shí)世界的網(wǎng)絡(luò),我們比較各個(gè)方法得出的邊重要性的分布、抽取的骨干結(jié)構(gòu)的大小,揭示方法之間的相關(guān)性。在比較了8種方法之后,我們提出了一種全局和局部自適應(yīng)的骨干結(jié)構(gòu)抽取方法(GLANB)。GLANB方法利用基于最短路徑的邊參與度和統(tǒng)計(jì)假設(shè)來(lái)評(píng)估邊的統(tǒng)計(jì)重要性；然后GLANB方法利用邊的統(tǒng)計(jì)重要性來(lái)保留更重要的邊,以此抽取骨干結(jié)構(gòu),骨干結(jié)構(gòu)是抽取后的有更少的節(jié)點(diǎn)和邊的子網(wǎng)絡(luò)。GLANB方法通過(guò)綜合的考慮網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)、邊的權(quán)重和節(jié)點(diǎn)度來(lái)決定邊的重要性,那些權(quán)重較小但在拓?fù)浣Y(jié)構(gòu)中起到重要作用的邊不會(huì)被輕視。GLANB方法可以被應(yīng)用到所有類(lèi)型的網(wǎng)絡(luò),包括加權(quán)/無(wú)權(quán)和有向/無(wú)向網(wǎng)絡(luò)。四個(gè)真實(shí)網(wǎng)絡(luò)的實(shí)證研究表明GLANB方法提出的邊重要性分布是雙峰的,因此可以得到邊的魯棒性分類(lèi)。進(jìn)一步地,GLANB方法傾向于將k-殼分解中網(wǎng)絡(luò)中心的節(jié)點(diǎn)保留在骨干結(jié)構(gòu)中。 綜上所述,GLANB方法可以幫助我們更好的理解網(wǎng)絡(luò)的結(jié)構(gòu),決定在信息傳遞中起關(guān)鍵作用的邊,并且通過(guò)骨干結(jié)構(gòu)的方式更方便的傳遞網(wǎng)絡(luò)所表達(dá)的信息。
[Abstract]:Complex networks are a useful tool for representing and analyzing complex systems, such as the World wide Web and transport systems. However, as the volume of complex networks increases, it becomes more and more difficult to understand the topology and their characteristics of networks. In this paper, we first review eight methods used to detect and sort critical edges in a network, and analyze their applicability and limitations. Then we apply these methods to four real-world networks. We compare the distribution of edge importance and the size of the extracted backbone structure to reveal the correlation between the methods. After comparing the eight methods, we propose a global and local adaptive backbone structure extraction method (GLANB). GLANB) to evaluate the statistical importance of edges by using the shortest path based edge participation and statistical assumptions. Then the GLANB method uses the statistical importance of edges to retain more important edges to extract the backbone structure, which is the extracted sub-network with fewer nodes and edges. The GLANB method considers the topological structure of the network synthetically. The importance of edges is determined by the weight of edges and the degree of nodes. The edges with small weights but which play an important role in topology can not be ignored. The GLANB method can be applied to all types of networks, including weighted / unauthorized and directed / undirected networks. Empirical studies on four real networks show that the edge importance distribution proposed by GLANB method is bimodal, so the robust classification of edges can be obtained. Furthermore, the GLANB method tends to retain the nodes of the network center in the k- shell decomposition in the backbone structure. To sum up, GLANB method can help us understand the structure of the network better, determine the edge that plays a key role in the transmission of information, and transfer the information expressed by the network more conveniently through the backbone structure.
【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】：2015
【分類(lèi)號(hào)】：O157.5

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本文編號(hào)：2408218