LAUSR

Graph partitioning methods in data mining have been widely used to discover protein complexes in protein–protein interaction (PPI) network. However, PPI networks with attributes need more effective attribute graph partitioning methods. Attribute graph partitioning aims to obtain high quality partitions satisfying the requirement: nodes in the same partition not only connect to each other more densely but also share more similar attribute values. In this paper, we propose a novel method for attributed graph partitioning based on fuzzy clustering. This method firstly devises a unified similarity measure using SimRank to construct the fuzzy similarity matrix of the attributed graph and can integrate structural and attribute similarities of nodes into a flexible weighted framework. Then it deduces the corresponding fuzzy equivalent matrix using fuzzy set theory. Finally, the result of partitioning can be obtained using fuzzy clustering algorithm. We conduct some experiments on several typical attributed graphs, which can also simulate PPI networks with attributes. The results show that our method is very effective to identify high quality partitions of attributed graphs and even performs better than some representative methods.