Incorporating User's Preference into Attributed Graph Clustering : Extended abstract.

In contrast to global graph clustering, local graph clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs). Currently, very few methods can deal with this kind of task. To this end, we propose two quality measures for a local graph cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). They measure the homogeneity/unimodality of the graph structure and the subspace that is composed of the designated attributes, respectively. We call their linear combination Compactness. Further, we propose LOCLU to optimize the Compactness score in order to find a good local graph cluster. The local graph cluster detected by LOCLU concentrates on the region of interest, provides efficient information flow in the graph, and exhibits a unimodal data distribution in the subspace of the designated attributes.