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Reinforced random walks are random walks on graphs whose transition probabilities along edges from a vertex are proportional to the weights of those edges, but where the weight of an edge evolves in a way that depends on the past traversals…

Information Theory · Computer Science 2026-05-22 Qinghua , Ding , Venkat Anantharam

We consider a vertex reinforced random walk on the integer lattice with sub-linear reinforcement. Under some assumptions on the regular variation of the weight function, we characterize whether the walk gets stuck on a finite interval. When…

Probability · Mathematics 2012-07-18 Anne-Laure Basdevant , Bruno Schapira , Arvind Singh

We prove that the restriction of the vertex-reinforced jump process to a subset of the vertex set is a mixture of vertex-reinforced jump processes. A similar statement holds for the non-linear hyperbolic supersymmetric sigma model. This is…

Probability · Mathematics 2024-11-12 Margherita Disertori , Franz Merkl , Silke W. W. Rolles

We consider a linearly edge-reinforced random walk on a class of two-dimensional graphs with constant initial weights. The graphs are obtained from $\mathbb{Z}^2$ by replacing every edge by a sufficiently large, but fixed number of edges in…

Probability · Mathematics 2009-10-13 Franz Merkl , Silke W. W. Rolles

We consider a multi-particle generalization of linear edge-reinforced random walk (ERRW). We observe that in absence of exchangeability, new techniques are needed in order to study the multi-particle model. We describe an unusual coupling…

Probability · Mathematics 2007-05-23 Yevgeniy Kovchegov

We prove that the edge-reinforced random walk on the ladder ${\mathbb{Z}\times\{1,2\}}$ with initial weights $a>3/4$ is recurrent. The proof uses a known representation of the edge-reinforced random walk on a finite piece of the ladder as a…

Probability · Mathematics 2007-05-23 Franz Merkl , Silke W. W. Rolles

We consider a class of strongly edge-reinforced random walks, where the corresponding reinforcement weight function is nondecreasing. It is known, from Limic and Tarr\`{e}s [Ann. Probab. (2007), to appear], that the attracting edge emerges…

Probability · Mathematics 2016-09-07 Codina Cotar , Vlada Limic

Vertex-reinforced random walk is defined in Pemantle's (1988) thesis; it is a random walk that is biased to visit sites it has already visited a lot. We show that this reinforcement scheme, in contrast to the scheme of edge-reinforcement,…

Probability · Mathematics 2016-09-07 Robin Pemantle , Stanislav Volkov

We consider the edge-reinforced random walk with multiple (but finitely many) walkers which influence the edge weights together. The walker which moves at a given time step is chosen uniformly at random, or according to a fixed order.…

Probability · Mathematics 2023-11-16 Nina Gantert , Fabian Michel , Guilherme Reis

We consider a class of multi-particle reinforced interacting random walks. In this model, there are some (finite or infinite) particles performing random walks on a given (finite or infinite) connected graph, so that each particle has…

Probability · Mathematics 2013-03-26 Jun Chen

The edge-reinforced random walk (ERRW) is a random process on the vertices of a graph that is more likely to cross the edges it has visited in the past. Depending on the strength of the reinforcement, the ERRW of a single particle can…

Probability · Mathematics 2025-09-23 Giordano Giambartolomei , Nadia Sidorova

We study the stability of compensated jump integrals under convergence of quadratic variation alone. Let \(X\) and \(\{X^n\}_{n\ge1}\) be c\`adl\`ag processes with jump measures \(\mu,\mu_n\) and predictable compensators \(\nu,\nu_n\).…

Probability · Mathematics 2026-05-26 Philip Kennerberg

We present the first rigorous quantitative analysis of once-reinforced random walks (ORRW) on general graphs, based on a novel change of measure formula.~This enables us to prove large deviations estimates for the range of the walk to have…

Probability · Mathematics 2025-09-05 Andrea Collevecchio , Pierre Tarrès

This article introduces a model for interacting vertex-reinforced random walks, each taking values on a complete sub-graph of a locally finite undirected graph. The transition probability for a walk to a given vertex depends on the…

Probability · Mathematics 2025-08-25 Fernando P. A. Prado , Rafael A. Rosales

We introduce a simple technique for proving the transience of certain processes defined on the random tree $\mathcal{G}$ generated by a supercritical branching process. We prove the transience for once-reinforced random walks on…

Probability · Mathematics 2007-05-23 Andrea Collevecchio

We describe a model for $m$ vertex reinforced interacting random walks on complete graphs with $d\geq 2$ vertices. The transition probability of a random walk to a given vertex depends exponentially on the proportion of visits made by all…

Probability · Mathematics 2022-03-22 Benito Pires , Fernando P. A. Prado , Rafael A. Rosales

We give a short proof of Theorem 2.1 from [MR07], stating that the linearly edge reinforced random walk (ERRW) on a locally finite graph is recurrent if and only if it returns to its starting point almost surely. This result was proved in…

Probability · Mathematics 2009-11-30 Laurent Tournier

This thesis examines linearly edge-reinforced random walks on infinite trees. In particular, recurrence and transience of such random walks on general (fixed) trees as well as on Galton-Watson trees (i.e. random trees) is characterized, and…

Probability · Mathematics 2023-09-01 Fabian Michel

For a numerical sequence ${a_n}$ satisfying broad assumptions on its "behaviour on average" and a random walk $S_n=\xi_1 +...+\xi_n$ with i.i.d. jumps $\xi_j$ with positive mean $\mu$, we establish the asymptotic behaviour of the sums…

Probability · Mathematics 2012-08-29 Alexander A. Borovkov , Konstantin A. Borovkov

Reinforced random walks (RRWs), including vertex-reinforced random walks (VRRWs) and edge-reinforced random walks (ERRWs), model random walks where the transition probabilities evolve based on prior visitation history~\cite{mgr, fmk,…

Machine Learning · Statistics 2026-05-22 Qinghua , Ding , Venkat Anantharam