Incorporating User's Preference into Attributed Graph Clustering

Incorporating User's Preference into Attributed Graph Clustering


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.

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Additional Information

Publisher Copyright: © 1989-2012 IEEE.

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  • Ye, Wei
  • Mautz, Dominik
  • Böhm, Christian
  • Singh, Ambuj
  • Plant, Claudia
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Journal Paper
Data Mining and Machine Learning
Journal or Publication Title
IEEE Transactions on Knowledge and Data Engineering
Page Range
pp. 3716-3728
December 2021
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