基于标记关系的多标记社团发现算法

Multi-label Community Detection Algorithm based on Multi-label Relationship

真实世界的对象具有多义性,具有非单一的多种标记。对于多标记的学习,现阶段的工作虽然能够利用标记间的重用评分分析多标记间的关系,但是尚不能直观挖掘出多标记的关系结构,也不能准确掌握多标记的主从关系以及多标记的重要性排名情况。而非负矩阵分解(Nonnegative matrix factorization,NMF)方法能对有关联的节点进行有效的社团划分,发掘关联节点的潜在关系,因此利用NMF方法对多标记关系进行社团结构分解成为有价值的研究内容。本文提出多标记社团发现算法,有效地对多标记进行挖掘,发现其中的社团结构,得到多标记的社团关系,并且能够对多标记节点的重要程度排序,分析多标记的主从结构,验证多标记关系算法的有效性,挖掘出其中隐藏的价值,这对于多标记的研究具有重要意义。

The objects of the real world can be assigned multiple meaning,with a variety of non-single labels.As to multi-label learning,although the related current work may take advantage of the reuse score to analyze the relationship between multiple labels,it still can find neither the label structure nor the main labels and importance rankings.The nonnegative matrix factorization（NMF）method can divide associated nodes into societies effectively,and explore the potential relationship between them.Consequently,it is worth studying how to use NMF in multi-label community detection.Here,an algorithm is proposed for multi-label community detection,which can analysis labels effectively and discover the community structure inside,and then obtain relations community.Besides,these multi-label nodes can be sorted according to their importance scores,and then the master-slave structure of these marked nodes can be obtained and the effectiveness of this algorithm is thus verified,which helps us learn the hidden information.