Traditional and Lazy PageRanks for a Line of Nodes Connected with Complete Graphs

Biganda, Pitos Seleka; Abola, Benard; Engström, Christopher; Mango, John Magero; Kakuba, Godwin; Silvestrov, Sergei

doi:10.1007/978-3-030-02825-1_17

Cited by 7 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, variants of PageRank in relation to some specific networks has been studied, e.g. in [8] and [9]; and also updating PageRank due to changes in some network is in literature, for instance, in [1] and [2].…”

Section: Introductionmentioning

confidence: 99%

Perturbed Markov Chains and Information Networks

Abola,

Biganda,

Silvestrov

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The paper is devoted to studies of perturbed Markov chains commonly used for description of information networks. In such models, the matrix of transition probabilities for the corresponding Markov chain is usually regularised by adding a special damping matrix multiplied by a small damping (perturbation) parameter ε. We give effective upper bounds for the rate of approximation for stationary distributions of unperturbed Markov chains by stationary distributions of perturbed Markov chains with regularised matrices of transition probabilities, asymptotic expansions for approximating stationary distributions with respect to damping parameter, as well as explicit upper bounds for the rate of convergence in ergodic theorems for n-step transition probabilities in triangular array mode, where perturbation parameter ε → 0 and n → ∞, simultaneously. The results of numerical experiments are also presented.

show abstract