An Improved Community Detection Algorithm Based on the Distance Dynamics
To solve the slow convergence or non convergence problem of traditional distance dynamics method, an improved community detection algorithm based on distance dynamics is proposed in this paper for speeding up the time efficient and accuracy of algorithm. Our improved algorithm firstly analyzes the node cohesiveness and slow convergence problems. Secondly, the neighbor cohesion is defined and two new interaction patterns are designed to enhance the accuracy of community. Finally, the convergence coefficient is defined to judge whether the dynamic interaction process is in the slow convergence or non convergence status. In the dynamic interaction process, when the percentage of non converged distance is less than convergence coefficient, the final value of the distances are pre judged and the whole algorithm is over. The experimental results show our improved algorithm has a fast convergent speed and accurate detection results.
2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)