Structure and Attributes Community Detection Benchmark and a Novel Selection Method

摘要

In recent years due to the rise of social, biological, and other rich content graphs, several new graph clustering methods using structure and node's attributes have been introduced. In this paper, we proposed an effective benchmark to evaluate these new methods. Our benchmark is an attributes extension to a widely used structure only benchmark. We also developed a new clustering method, termed Selection method, that uses the graph structure ambiguity to switch between structure and attribute clustering methods. Using the new benchmark and Normalized Mutual Information (NMI) metric, we evaluated the Selection method against five clustering methods: three structure and attribute methods, one structure only method and one attribute only method. We showed that the Selection method outperformed that state-of-art structure and attribute methods.