Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Matrix computations (3rd ed.)
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
Greedily finding a dense subgraph
Journal of Algorithms
A clustering algorithm based on graph connectivity
Information Processing Letters
Approximation algorithms for maximization problems arising in graph partitioning
Journal of Algorithms
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
On the densest k-subgraph problems
On the densest k-subgraph problems
Functional topology in a network of protein interactions
Bioinformatics
Protein complex prediction via cost-based clustering
Bioinformatics
Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks
International Journal of Bioinformatics Research and Applications
| Hi-index | 0.00 |
Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k{\hbox{-}}\rm Subgraph Problem (k{\hbox{-}}\rm HSP), which itself is NP-hard. However, any method based on the k{\hbox{-}}\rm HSP requires the parameter k and an exact solution of k{\hbox{-}}\rm HSP may still end up as a "spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in "spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.