The Study Of Clustering Coefficient Of Random Network

Random network is a new kind of system science. It regards systems with complex macrostructure as a network, analyzes them structural characters, such as topological indices, cause of formation, evolution and applications. Popularity of complex network thanks to excellent researches on two models: one is the small-word network model introduced by Watts and Strogatz, it reveals the importance of average path length and clustering coefficient, the other one is the scale-free network model introduced by Albert and Barabasi, it reveals the relationship between complex networks and degree distribution. These three spectacular concepts play a key role in recent study and development of complex network theory. They and some other properties influenced by them reveal the explicit ordered facts behind complex phenomena. Researches of complex network are always aim to discover these facts.In this paper, we study the random clustering coefficient in the network. Firstly we find the inadequacies of the original definition, and then give a more reasonable definition with more innovations. It is compatible with the previous clustering coefficient definitions, and makes it reasonable. Comparing the new cluster coefficient definition with the previous, combi-nating the graphics, demonstrating the reasonablility of the new definition, clustering coefficient definition makes more suitable for random network research.