Discovering top-k non-redundant clusterings in attributed graphs

Many graph clustering algorithms focus on producing a single partition of the vertices in the input graph. Nevertheless, a single partition may not provide sufficient insight about the underlying data. In this context, it would be interesting to explore alternative clustering solutions. Many areas, such as social media marketing demand exploring multiple clustering solutions in social networks to allow for behavior analysis to find, for example, potential customers or influential members according to different perspectives. Additionally, it would be desirable to provide not only multiple clustering solutions, but also to present multiple non-redundant ones, in order to unleash the possible many facets from the underlying dataset. In this paper, we propose RM-CRAG, a novel algorithm to discover the top-k non-redundant clustering solutions in attributed graphs, i.e., a ranking of clusterings that share the least amount of information, in the information theoretic sense. We also propose MVNMI, an evaluation criterion to assess the quality of a set of clusterings. Experimental results using different datasets show the effectiveness of the proposed algorithm.