원문정보
초록
영어
This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.
목차
1. Introduction
2. IGALO Algorithm
2.1. Objective Functions
2.2. Encoding Method
2.3. Initial Population
2.4. Crossover Strategy
2.5. Mutation Strategy
2.6. Description of IGALO Algorithm
2.7. Analysis of Algorithm Time Complexity
3. Experiments
3.1. Evaluation Criteria
3.2. Dataset Experiment of Benchmark Networks
3.3. Dataset of Real-World Networks
4. Conclusion
References