南京大学学报(自然科学版) ›› 2014, Vol. 50 ›› Issue (4): 466.
柴变芳1,2*, 赵晓鹏3, 贾彩燕1, 于 剑1
Chai Bianfang1,2, Zhao Xiaopeng3, Jia Caiyan1, Yu Jian1
摘要: 研究表明将边表示的网络转换为三角形模体表示形式,可以有效解决基于模型社区发现方法由网络规模庞大带来的计算瓶颈问题。提出一个三角形模体社区发现模型MCDTM(a Model for Community Detection based on Triangular Motifs),其将网络表示为一系列三角形模体,利用categorical分布对各三角形模体的生成过程建模,用最大似然参数估计方法给出参数估计的推理过程,根据参数估计结果可得节点、边及三角形模体的社区隶属度。人工网络和实际网络上的实验证明MCDTM模型可快速准确地发现网络的潜在结构。
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