A multiobjective evolutionary algorithm to find community structures based on affinity propagation
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
Community detection plays an important role in reflecting and understanding the topological structure of complex networks, and can be used to help mine the potential information in networks. This paper presents a Multiobjective Evolutionary Algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection. Firstly, APMOEA takes the method of affinity propagation (AP) to initially divide the network. To accelerate its convergence, the multiobjective evolutionary algorithm selects nondominated solutions from the preliminary partitioning results as its initial population. Secondly, the multiobjective evolutionary algorithm finds solutions approximating the true Pareto optimal front through constantly selecting nondominated solutions from the population after crossover and mutation in iterations, which overcomes the tendency of data clustering methods to fall into local optima. Finally, APMOEA uses an elitist strategy, called “external archive”, to prevent degeneration during the process of searching using the multiobjective evolutionary algorithm. According to this strategy, the preliminary partitioning results obtained by AP will be archived and participate in the final selection of Pareto-optimal solutions. Experiments on benchmark test data, including both computer-generated networks and eight real-world networks, show that the proposed algorithm achieves more accurate results and has faster convergence speed compared with seven other state-of-art algorithms.
|Journal||Physica A: Statistical Mechanics and its Applications|
|Early online date||26 Feb 2016|
|Publication status||Published - 1 Jul 2016|
- Complex network, Community detection, Affinity propagation, Multiobjective evolutionary algorithm