DataGen: a generator of datasets for evaluation of classification algorithms
Pattern Recognition Letters
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Algorithms for graph partitioning on the planted partition model
Random Structures & Algorithms
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
The Effect of Network Realism on Community Detection Algorithms
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
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 compare our novel clustering method, termed Selection method, against seven clustering methods: three structure and attribute methods, one structure only method, one attribute only method, and two ensemble methods. The Selection method uses the graph structure ambiguity to switch between structure and attribute clustering methods. We shows that the Selection method out performed the state-of-art structure and attribute methods.