计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 56-62.doi: 10.11896/jsjkx.220100155
曾祥宇, 龙海霞, 杨旭华
ZENG Xiangyu, LONG Haixia, YANG Xuhua
摘要: 社区结构普遍存在于自然界的各种复杂网络中,是网络结构的重要特征之一。社区发现算法可以识别网络中的有用信息,有助于分析网络的结构和功能,被广泛应用于社交网络、生物和医学等领域。文中针对目前基于局部相似性的复杂网络社区发现算法精确度不高的问题,提出了一种基于马尔可夫相似性增强和网络嵌入的社区发现算法。首先,受马尔可夫链思想启发,提出了一种马尔可夫相似性增强方法,通过对初始网络的马尔可夫迭代状态进行转移,来获取稳态的马尔可夫相似性增强矩阵,根据马尔可夫相似性指标对网络进行初始的社区划分。然后结合网络的拓扑结构和网络嵌入,提出了一种新的社区相似性指标,将初始社区结构中的小社区与其连接紧密的社区合并,得到网络社区结构。在7个真实网络和可变参数的人工网络上,通过与其他5个知名社区发现算法的比较,证明了所提算法具有良好的社区发现效果。
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