A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Data structures for weighted matching and nearest common ancestors with linking
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality matching and local improvement for multilevel graph-partitioning
Parallel Computing - Special issue on graph partioning and parallel computing
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
A fast kernel-based multilevel algorithm for graph clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The architecture of a proteomic network in the yeast
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
A multi-level approach for document clustering
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Multilevel approaches for large-scale proteomic networks
International Journal of Computer Mathematics - Bioinformatics
A spectral clustering algorithm for manufacturing cell formation
Computers and Industrial Engineering
| Hi-index | 0.00 |
Identifying functional modules is believed to reveal most cellular processes. There have been many computational approaches to investigate the underlying biological structures (Bader and Hogue, 2003; Dhillon et al., 2005; Krogan et al., 2006; Ramadan et al., 2005; Xiong et al., 2005; Zhang et al., 2004). A spectral clustering method plays a critical role identifying functional modules in a yeast protein-protein network (Ramadan et al., 2005). We present an unweighted-graph version of a multilevel spectral algorithm which more accurately identifies protein complexes with less computational time.