DataGen: a generator of datasets for evaluation of classification algorithms
Pattern Recognition Letters
Algorithms for graph partitioning on the planted partition model
Random Structures & Algorithms
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
A model-based approach to attributed graph clustering
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Getting Clusters from Structure Data and Attribute Data
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
| Hi-index | 0.00 |
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.