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Tight Bounds for Quasirandom Rumor Spreading

Tight bounds for quasirandom rumor spreading
Authors: Angelopoulos, Spyros; Doerr, Benjamin; Huber, Anna; Panagiotou, Konstantinos;

Tight Bounds for Quasirandom Rumor Spreading

Abstract

This paper addresses the following fundamental problem: Suppose that in a group of $n$ people, where each person knows all other group members, a single person holds a piece of information that must be disseminated to everybody within the group. How should the people propagate the information so that after short time everyone is informed? The classical approach, known as the push model, requires that in each round, every informed person selects some other person in the group at random, whom it then informs. In a different model, known as the quasirandom push model, each person maintains a cyclic list, i.e., permutation, of all members in the group (for instance, a contact list of persons). Once a person is informed, it chooses a random member in its own list, and from that point onwards, it informs a new person per round, in the order dictated by the list. In this paper we show that with probability $1-o(1)$ the quasirandom protocol informs everybody in $(1 \pm o(1))\log_2 n+\ln n$ rounds; furthermore we also show that this bound is tight. This result, together with previous work on the randomized push model, demonstrates that irrespectively of the choice of lists, quasirandom broadcasting is as fast as broadcasting in the randomized push model, up to lower order terms. At the same time it reduces the number of random bits from $O(\log^2 n)$ to only $\lceil\log_2 n\rceil$ per person.

Country
France
Keywords

quasirandom protocol, Probability in computer science (algorithm analysis, random structures, phase transitions, etc.), quasirandom broadcasting, Network protocols, [INFO] Computer Science [cs]

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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!
10
Average
Top 10%
Top 10%
gold