基于粒子群优化算法的社交网络结构平衡的实现

Realization of social network structural balance based on particle swarm optimization algorithm

社交网络结构平衡的研究具有重要的理论研究和实际应用价值。然而经典粒子群优化算法主要用于求解连续优化问题,无法直接用于离散优化问题的求解。针对社交网络中的平衡问题,借助群体智能算法的思想,将社交网络结构平衡问题建模成数学优化问题,设计了一种高效的离散粒子群优化算法求解算法模型,借助社交网络的拓扑结构,采用重新定义粒子的离散表示,重新设计离散的粒子状态更新方程。为了验证所提算法的有效性,在模拟社交网络数据上对算法进行了测试。实验表明,所提出的离散粒子群优化算法不仅可以实现网络的结构平衡,还可以挖掘网络中存在的社区结构。

The research on the balance of social network structure has important theoretical research and practical application value.However,the classic particle swarm optimization algorithm is mainly used to solve continuous optimization problems,and cannot be directly used to solve discrete optimization problems.Aiming at the balance problem in social networks,this thesis uses the idea of swarm intelligence algorithm to model the structural balance of social network as a mathematical optimization problem,and designs an efficient discrete particle swarm optimization algorithm to solve the algorithm model,with the help of the topology of the social network the structure,using the redefinition of the discrete representation of particles,redesigning the discrete particle state update equation.In order to verify the effectiveness of the proposed algorithm,the algorithm was tested on simulated social network data.Experiments show that the proposed discrete particle swarm optimization algorithm can not only achieve the structural balance of the network,but also mine the community structure existing in the network.