Research On Community Detection In Complex Network

As one of the important direction in the field of data mining, complex networks have received extensive attention of a number of scholars both at home and abroad. In the past years, considerable research has been conducted on community detection, link prediction and dynamic problems. This paper mainly focuses on community detection algorithm, in which the typical community detection algorithm is analyzed, the Dense Shrink algorithm is improved and the Improved Dense Shrink algorithm is proposed. Meanwhile, this paper extends the model used in general network to signed network, then proposes a dynamics-based community detection algorithm; Finally, according to the characteristics of signed network, we propose a similarity-based community detection algorithm. The research includes:In this paper, we propose the simplified Katz similarity through the analysis of varies similarities, which not only have little influence on accuracy, but also can shorten the time of calculation. Compare to merge dense pair, the most time-consuming step-- merge micro community does not always perform well in the Dense Shrink algorithm, and using the similarity of more information can reduce the probability of micro communities. Therefore,this paper puts forward the Improved Dense Shrink algorithm by using SKatz similarity and replacing the step of merging micro communities with merging dense pairs.This paper introduces the negative coupling coefficient which extends the network model to adapt to the characteristics of signed network through the analysis of the general used network model. The positive coupling coefficient is to make the nodes with positive connection tend to be closer; and the negative coupling coefficient is to make the nodes with negative connection far away from each other. At the same time, the value of the coefficient(both positive and negative coupling coefficient) in the synchronization equation and its influence to the synchronization result are analyzed. According to the characteristics of signed network, we propose a dynamics-based community detection algorithm.In this paper, the edges in signed network is analyzed, from which the symbol properties and connection properties are found. The symbol properties correspond to the positive and negative attribute of the weights of the edges, which means whether the relationship between the nodes is positive or negative; and the connection properties correspond whether the weights of the edges is zero or nonzero, the property represents the existence of the relationship between nodes. According to the two properties, a similarity matched the characteristics of signed network is formed, then we propose the similarity-based community detection algorithm combined with the idea of modularity optimization.