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Better Bounds for Coalescing-Branching Random Walks

Authors: Michael Mitzenmacher; Rajmohan Rajaraman; Scott T. Roche;

Better Bounds for Coalescing-Branching Random Walks

Abstract

Coalescing-branching random walks, or cobra walks for short, are a natural variant of random walks on graphs that can model the spread of disease through contacts or the spread of information in networks. In a k -cobra walk, at each timestep, a subset of the vertices are active; each active vertex chooses k random neighbors (sampled independently and uniformly with replacement) that become active at the next step, and these are the only active vertices at the next step. A natural quantity to study for cobra walks is the cover time, which corresponds to the expected time when all nodes have become infected or received the disseminated information. In this article, we extend previous results for cobra walks in multiple ways. We show that the cover time for the 2-cobra walk on [0, n ] d is O ( n ) (where the order notation hides constant factors that depend on d ); previous work had shown the cover time was O ( n ⋅polylog( n )). We show that the cover time for a 2-cobra walk on an n -vertex d -regular graph with conductance φ G is O ( d 4 φ −2 G log 2 n ), significantly generalizing a previous result that held only for expander graphs with sufficiently high expansion. And, finally, we show that the cover time for a 2-cobra walk on a graph with n vertices and m edges is always O ( mn 3/4 log n ; this is the first result showing that the bound of Θ( n 3 ) for the worst-case cover time for random walks can be beaten using 2-cobra walks.

Keywords

FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Top 10%
Green
bronze