Incorporating User's Preference into Attributed Graph Clustering
This addresses a domain-specific need for users who want to find a single local cluster based on prior knowledge, rather than partitioning the entire graph, which is incremental as it builds on existing graph clustering methods.
The paper tackles the problem of local clustering on attributed graphs, where only a single cluster around a user-specified seed vertex and attributes is sought, by proposing LOCLU to optimize a Compactness score based on graph and attribute homogeneity, achieving results that concentrate on the region of interest with efficient information flow and unimodal attribute distribution.
Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. Sometimes, based on domain knowledge, people may have information about a specific target region in the graph and only want to find a single cluster concentrated on this local region. Such a task is called local clustering. In contrast to global clustering, local 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 cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). The former measures the homogeneity of the graph structure while the latter measures the homogeneity of the subspace that is composed of the designated attributes. We call their linear combination as Compactness. Further, we propose LOCLU to optimize the Compactness score. The local 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.