An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
SIAM Journal on Computing
Journal of Computer and System Sciences
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Ranking systems: the PageRank axioms
Proceedings of the 6th ACM conference on Electronic commerce
Applications of Membrane Computing (Natural Computing Series)
Applications of Membrane Computing (Natural Computing Series)
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Cycles and communicating classes in membrane systems and molecular dynamics
Theoretical Computer Science
Bringing PageRank to the citation analysis
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
The page rank of a webpage is a numerical estimate of its authority. In Google's PageRank algorithm the ranking is derived as the invariant probability distribution of a Markov chain random surfer model. The crucial point in this algorithm is the addition of a small probability transition for each pair of states to render the transition matrix irreducible and aperiodic. The same idea can be applied to P systems, and the resulting invariant probability distribution characterizes their dynamical behavior, analogous to recurrent states in deterministic dynamical systems. The modification made to the original P system gives rise to a new class of P systems with the property that their computations need to be robust against random mutations. Another application is the pathway identification problem, where a metabolite graph is constructed from information about biochemical reactions available in public databases. The invariant distribution of this graph, properly interpreted as a Markov chain, should allow to search pathways more efficiently than current algorithms. Such automatic pathway calculations can be used to derive appropriate P system models of metabolic processes.