過去幾十年來很多複雜網路都被研究分析，在分析中，叢聚度是重要的概念，有關網路模型緊密性的一個主要特徵，它是一個重要的網路統計數值在許多真實網路，尤其是近幾年發展迅速的社群網路、通訊網路。在此論文中提出在保持各個節點的分支度不變的情況下用維持連線數不變的連線重接演算法和區域連線交換技術來增加網路叢聚度，所以它可以被廣泛的用在製造出相似的模型從已選出的網路模型。這個演算法是基於區域鄰接點的資訊來執行。如何在有向和有權重的網路中執行這演算法將是本論文的研究重點。 Over the past decade the studies of complex networks have been analyzed and researched. In analyzing Clustering coefficient is a important concept Clustering coefficient characterizes the relative tightness of a network and is a defining network statistics that appears in many “real-world” network data. This paper proposed a local link switching algorithm which effectively increases the clustering coefficient of a directed weight network while preserving the network node degree distributions. This link switching algorithm is based on local neighborhood information. Link switching algorithm is widely used in producing similar networks with the same degree distribution, that is, it is used in ‘sampling’ networks from the same network pool. How to use this algorithm to implement in directed and weight network is major study in this paper.
