抄録
Graph clustering is a long-standing problem in data mining and machine learning. Traditional graph clustering aims to partition a graph into several densely connected components. However, with the proliferation of rich attribute information available for objects in real-world graphs, vertices in graphs are often associated with a number of attributes that describe the properties of the vertices. This gives rise to a new type of graphs, namely attributed graphs. Thus, how to leverage structural and attribute information becomes a new challenge for attributed graph clustering. In this paper, we introduce the state-of-the-art studies on clustering large attributed graphs. These methods propose different approaches to leverage both structural and attribute information. The resulting clusters will have both cohesive intra-cluster structures and homogeneous attribute values.
