基于模体的模块度优化高阶社区检测算法

A Higher-Order Community Detection Algorithm Based on Motif-Based Modularity Optimization

为提升现有高阶社区检测算法的性能,提出一种基于模体的模块度优化高阶社区检测算法。通过量化网络模体数量为节点间权重,将高阶基于模体的社区检测转化为低阶基于边的加权网络社区检测,并构造为加权模块度优化问题求解。为保证加权模块度优化性能以获得逼近全局最优的高阶社区划分,一方面优化策略采用典型的生物启发式算法;另一方面综合利用网络低阶和高阶拓扑结构信息,通过节点邻域社区修正操作和局部搜索操作,进一步提升社区划分质量并防止算法陷入局部最优。在人工合成和真实世界网络上的实验表明,利用模体高阶结构信息有助于提升复杂网络社区的检测性能,尤其是在社区结构较为模糊的情况下。该算法能够有效实现基于模体的高阶社区检测,与现有基于模体的典型方法相比在精确性和质量性上表现出一定优势,有助于加深对网络高阶结构及功能特性的理解。

In order to improve the performance of existing higher-order community detection algorithms,a higher-order community detection algorithm based on motif-based modularity optimization is proposed.By quantifying the number of motifs as the weight between nodes,the higher-order community detection based on motifs is transformed into lower-order weighted network community detection based on edges,and a weighted modularity optimization problem is constructed.Based on the meta-heuristic algorithm as the optimization strategy,the lower-order topology structure and higher-order weight information are comprehensively utilized to design the neighborhood community modification operation and local search operation of nodes,so as to improve the quality of community partitions and prevent the algorithm from falling into local optimum.Experimental results on synthetic and real-world networks show that the utilization of motifs is helpful to improve the detection performance under the condition of fuzzy community structure.The proposed algorithm can effectively realize motif-based community detection and has certain advantages in accuracy and quality compared with existing typical motif-based algorithms,which helps to deepen the understanding of the higher-order structure and functional characteristics of complex networks.