局部协同选择聚类的多视角社区发现研究

近年来，随着各种网络应用平台愈演愈烈，多种关系网络中用户之间往往存在大量相似的局部社区结构. 鉴于传统单视角社区发现算法在划分时无法同时考虑多种因素，本文将在多视角原理上提出一种基于局部协同选择聚类的多视角社区发现模型，该模型中主要解决了传统多视角聚类算法的条件限制问题（节点，聚类个数和充分的属性信息）和过度调整问题. 首先，构建选择调节矩阵来训练各视角中的共同部分节点集，并集成其共同节点的社团结构，然后，构建局部优化矩阵将共同节点结构做为训练集，利用核岭回归（KRR）原理完成各视角中孤立节点的划分，最后通过UCI数据集和DBLP数据集来分别验证聚类精度和算法适用性.

Research on Multi-View Community Detection Based on Local Co-Selecting Clustering

In recent year, with the development of various network platforms, there are always a lot of similar local community structures between users in different networks. In consideration of some single-view community detection algorithms cannot find the multi-factor community structures, in this paper we present a Multi-view Local collaborative Selecting Clustering model (called co-MLSC). This model can solve many constraints problems (like nodes, clusters, and sufficient information) and over adjustment problems. Firstly, the model can build a choice regulate matrix that can train the common part of the node set, and converge its common structure. Then we also build a local optimization matrix that regards the node structure as a training set, and uses the KRR algorithm to complete the division of isolated nodes. Finally, we use the UCI and DBLP data sets to demonstrate the effectiveness and applicability of our algorithm.