Name
Abstract | ||
|---|---|---|
We consider static and dynamic approaches to the specification of probability distributions on graphs, consistent with desired statistical properties such as degree distributions, for use in modeling biological networks. In the static approach we develop analytical approximations to the Hamiltonian and partition functions. In the dynamic approach, we use a stochastic parameterized grammar to construct an evolutionary tree in which the nodes represent elements such as genes or cells and the links represent inheritance relations between the nodes. The grammar then constructs a network based on the feature vectors of the nodes in the tree. (c) 2006 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1002/cplx.20140 | COMPLEXITY |
Keywords | Field | DocType |
stochastic grammars, statistical mechanics of networks | Phylogenetic tree,Partition function (mathematics),Hamiltonian (quantum mechanics),Computer science,Theoretical computer science,Probability distribution,Artificial intelligence,Feature vector,Parameterized complexity,Biological network,Algorithm,Grammar,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 6 | 1076-2787 |
Citations | PageRank | References |
2 | 0.61 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Bhan | 1 | 61 | 8.01 |
| Eric Mjolsness | 2 | 1058 | 140.00 |