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Let G=(V,E) be a connected graph with |V|=n vertices. A simple random walk on the vertex set of G is a process, which at each step moves from its current vertex position to a neighbouring vertex chosen uniformly at random. We consider a modified walk which, whenever possible, chooses an unvisited edge for the next transition; and makes a simple random walk otherwise. We call such a walk an edge‐process (or E‐process). The rule used to choose among unvisited edges at any step has no effect on our analysis. One possible method is to choose an unvisited edge uniformly at random, but we impose no such restriction. For the class of connected even degree graphs of constant maximum degree, we bound the vertex cover time of the E‐process in terms of the edge expansion rate of the graph G, as measured by eigenvalue gap 1−λmax of the transition matrix of a simple random walk on G. A vertex v is ℓ‐good, if any even degree subgraph containing all edges incident with v contains at least ℓ vertices. A graph G is ℓ‐good, if every vertex has the ℓ‐good property. Let G be an even degree ℓ‐good expander of bounded maximum degree. Any E‐process on G has vertex cover time CV(E−process)=O(n+nlognℓ). This is to be compared with the Ω(nlogn) lower bound on the cover time of any connected graph by a weighted random walk. Our result is independent of the rule used to select the order of the unvisited edges, which could, for example, be chosen on‐line by an adversary. As no walk based process can cover an n vertex graph in less than n – 1 steps, the cover time of the E‐process is of optimal order when ℓ=Θ(logn). With high probability random r‐regular graphs, r≥4 even, have ℓ=Ω(logn). Thus the vertex cover time of the E‐process on such graphs is Θ(n). © 2013 Wiley Periodicals, Inc. Random Struct. Alg., 46, 36–54, 2015 [ABSTRACT FROM AUTHOR]

We present a new randomized diffusion-based algorithm for balancing indivisible tasks (tokens) on a network. Our aim is to minimize the discrepancy between the maximum and minimum load. The algorithm works as follows. Every vertex distributes its tokens as evenly as possible among its neighbors and itself. If this is not possible without splitting some tokens, the vertex redistributes its excess tokens among all its neighbors randomly (without replacement). In this paper we prove several upper bounds on the load discrepancy for general networks. These bounds depend on some expansion properties of the network, that is, the second largest eigenvalue, and a novel measure which we refer to as refined local divergence. We then apply these general bounds to obtain results for some specific networks. For constant-degree expanders and torus graphs, these yield exponential improvements on the discrepancy bounds. For hypercubes we obtain a polynomial improvement. [ABSTRACT FROM AUTHOR]