Exploring Multiple Clusterings in Attributed Graphs

Many graph clustering algorithms aim at generating a single partitioning (clustering) of the data. However, there can be many ways a dataset can be clustered. From a exploratory analisys perspective, given a dataset, the availability of many different and non-redundant clusterings is important for data understanding. Each one of these clusterings could provide a different insight about the data. In this paper, we propose M-CRAG, a novel algorithm that generates multiple non-redundant clusterings from an attributed graph. We focus on attributed graphs, in which each vertex is associated to a n-tuple of attributes (e.g., in a social network, users have interests, gender, age, etc.). M-CRAG adds artificial edges between similar vertices of the attributed graph, which results in an augmented attributed graph. This new graph is then given as input to our clustering algorithm (CRAG). Experimental results indicate that M-CRAG is effective in providing multiple clusterings from an attributed graph.