The algorithm design manual
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Handbook of Graphs and Networks: From the Genome to the Internet
Handbook of Graphs and Networks: From the Genome to the Internet
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
A matrix-based multilevel approach to identify functional protein modules
International Journal of Bioinformatics Research and Applications
Triangular clique based multilevel approaches to identify protein functional modules
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Multilevel algorithms for partitioning power-law graphs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
The architecture of a proteomic network in the yeast
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
A multilevel approach to identify functional modules in a yeast protein-protein interaction network
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
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Our multilevel algorithms aim to improve existing graph clustering algorithms which predict protein complexes in large-scale proteomic networks, which are represented as unweighted graphs. Current matching based multilevel algorithms are hampered by low-quality of grouping (coarsening) even though they dramatically reduce computational time. We present a multilevel algorithm with structured analysis of unweighted networks which constructs high-quality groups of nodes merged before applying a clustering algorithm. A 2-core network of a proteomic network is constructed by removing all nodes which have degree less than two recursively. Our multilevel algorithm builds a series of smaller (or coarser) networks from the 2-core network by searching highly dense subgraphs in each level and then a clustering algorithm is applied. The clustering results are passed to the original network with additional fine tuning. All leftover nodes outside the 2-core network are added back after the multilevel algorithm. Compared to existing multilevel algorithm, our multilevel algorithm on 2-core networks shows that nodes in coarser networks have higher accuracy in all supernodes, and clustering results show up to 15% (mostly around 10%) improvements. Moreover, our clustering algorithm uses only one or two levels, so it is free from deciding the number of levels to expect best results.