Optimization of Jacobian matrix annealing algorithm based on hybrid model

Abstract

The annealing algorithm can effectively find the clustering distribution of network structure, but the algorithm accuracy of handling different networks needs to be further improved. In order to identify the data distribution in network structure of mixed model more accurately and solve the problems of local maximum value and convergence of mixed model, the Jacobian matrix annealing algorithm is studied. First, the model is initialized by using the inverse temperature parameter, and then the two tasks of expectation step and maximization step are performed iteratively. The posterior probability of model is calculated based on the Jacobian matrix until the algorithm reaches the set accuracy or meets the convergence condition. The Jacobian matrix annealing algorithm is compared with the inverse simulated annealing clustering algorithm under semi-supervised Gaussian mixture model on the real network, and the experimental results show that the accuracy of Jacobian matrix algorithm in the hybrid network model is better. The proposed algorithm can not only prevent the network from falling into the local optimum, but also improve the accuracy of analyzing network clustering distribution.