Role Discovery for Graph Clustering-猫眼课题宝

DOI：

10.1007/978-3-642-20291-9_5

发表时间：

2011-04

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作者：

Bin-Hui Chou;Einoshin Suzuki

图聚类是发现网络底层结构的重要任务。在过去的几十年中，诸如归一化割和基于模块的方法等众所周知的方法被开发出来。这些方法可以称为非重叠，因为它们假设顶点属于一个社区。另一方面，重叠的方法，如CPM，假设一个顶点可能属于一个以上的社区，已经引起了人们的注意，因为假设符合现实。我们认为，现有的重叠方法是过于简单的顶点位于社区的边界。也就是说，它们缺乏对将顶点连接到属于不同社区的邻居的边的仔细考虑。因此，我们提出了一种新的图聚类方法，命名为RoClust，它使用三种不同的角色，每种角色代表一种不同的顶点连接社区。实验结果表明，我们的方法优于国家的最先进的图聚类方法。

Graph clustering is an important task of discovering the underlying structure in a network. Well-known methods such as the normalized cut and modularity-based methods are developed in the past decades. These methods may be called non-overlapping because they assume that a vertex belongs to one community. On the other hand, overlapping methods such as CPM, which assume that a vertex may belong to more than one community, have been drawing attention as the assumption fits the reality. We believe that existing overlapping methods are overly simple for a vertex located at the border of a community. That is, they lack careful consideration on the edges that link the vertex to its neighbors belonging to different communities. Thus, we propose a new graph clustering method, namedRoClust, which uses three different kinds of roles, each of which represents a different kind of vertices that connect communities. Experimental results show that our method outperforms state-of-the-art methods of graph clustering.

Role Discovery for Graph Clustering

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