A community detection algorithm based on triadic closure and membership closure

With the development of network and the expansion of people's communication ways,the social network penetrates into almost every corner of the entire society,and changes the ways of information communicating and news sharing,attracting more and more people's attention and research on it.Social network,also named social networking service,originated from the translation of British SNS(social network service),was literally translated as social network services or social networking service in Chinese.There're many manifestations of social networking platforms,such as:QQ,WeChat,Facebook and Micro-blog. In this paper,we mainly focus on the micro-blog,the emerging social platform.The main purpose of research on micro-blog is to find out the various relationships between users.People generally believe there're mainly 5relations existing in miro-blog among users:the relationship of concerning,mentioning,forwarding,commenting and being friends.Due to the large number of social network users and the complicated relations among them,the generated social data,compared with the traditional data,has the characteristics of large amount of data,complex structure and semantically rich features.So according to the relationship among users,this paper proposes divided community algorithm based on triadic closure. In the first instance,this algorithm took the initial community as being empty,in which the vertex degree maximum among all vertices was chosen as the initial vertex,then requesting for the number of Triadic closures between the initial vertex and the adjacent vertex,and requesting for the probability of vertices belonged to the community.The vertex with the maximum Ps joined the initial vertex community,forming a new initial community.With continued iterating,and by using collection algorithm of triadic and membership closure,the remained few vertices could be divided into different communities until the entire community was completely divided.Finally,every community was intuitively and visually presented by Graphics.When using this algorithm,the number of Triadic closure,the probability of vertex belonged to a community and the difference in expansion degree are the keys to value vertices in complex network.This method combines the characteristics of the global importance of vertices.Namely,in complex networks,the greater the number of Triadic closure is,the greater the likelihood of them in a community will be.The greater the vertex membership closures are,the priorer the vertex will be divided.The difference in expansion degree is to determine whether the i community is divided completely or not. The research of social networking can not only help us understand and analyze the network structure,and detect to analyze network,but also can help link the relationship in virtual world to the real world.So the virtual relationship could be transferred into profits,providing valuable network for enterprises,and digging out the great economic value behind the social network.It can be embodied like this:firstly,to help companies find potential business opportunities by analyzing users' comments and published content to learn their consumption power,preferences and recent buying habits,thus to know the probability that he could purchase products.Second,it can give crisis warning message.According to user's information,products satisfaction degree of users can be learnt.Third,it can drive the information propagation speed and message breadth,of which the enterprises take advantage,achieving better product publicity. Compared with the community network of Bottlenose Dolphin Internet and Zachary,the algorithm mentioned in this paper was proved to be effective and feasible.