计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 204-211.doi: 10.11896/jsjkx.210300060
徐新黎, 肖云月, 龙海霞, 杨旭华, 毛剑飞
XU Xin-li, XIAO Yun-yue, LONG Hai-xia, YANG Xu-hua, MAO Jian-fei
摘要： 属性网络不但包含节点之间复杂的拓扑结构,还包含拥有丰富属性信息的节点,其可以比传统网络更有效地建模现代信息系统,属性网络的社区划分对于分析复杂系统的层次结构、控制信息在网络中的传播和预测网络用户的群体行为等方面具有重要的研究价值。为了更好地利用拓扑结构信息和属性信息进行社区发现,提出了一种基于矩阵分解的属性网络嵌入和社区发现算法(CDEMF)。首先提出基于矩阵分解的属性网络嵌入方法,基于网络局部链接信息计算相邻节点的相似性,将其与属性接近度联合建模,通过矩阵分解的分布式算法得到每个节点对应的低维嵌入向量,即把网络节点映射为低维向量表示的数据点集合。接着提出基于曲率和模块度的社区划分方法,自动确定数据点集合中蕴含的社区数量,并通过对数据点集合聚类完成属性网络社区划分。在真实网络数据集上,将CDEMF方法与其他8种知名算法进行比较,实验结果表明CDEMF具有良好的性能。
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