The Worst Case Behavior of Randomized Gossip

Baumann, Hervé; Fraigniaud, Pierre; Harutyunyan, Hovhannes A.; Verclos, Rémi de Joannis de

doi:10.1007/978-3-642-29952-0_34

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…The worst case behavior of the quasirandom model was very recently addressed by Baumann et al [4]. Among other results, the authors present a polynomial-time algorithm to compute the configuration of lists and initial neighbors which maximizes the time to spread the rumor.…”

Section: Results Obtained After This Workmentioning

confidence: 99%

Quasirandom Rumor Spreading

Doerr

Friedrich

Sauerwald

2014

ACM Trans. Algorithms

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We propose and analyze a quasirandom analogue of the classical push model for disseminating information in networks ("randomized rumor spreading"). In the classical model, in each round, each informed vertex chooses a neighbor at random and informs it, if it was not informed before. It is known that this simple protocol succeeds in spreading a rumor from one vertex to all others within O(log n) rounds on complete graphs, hypercubes, random regular graphs, Erdős-Rényi random graphs, and Ramanujan graphs with probability 1 − o(1). In the quasirandom model, we assume that each vertex has a (cyclic) list of its neighbors. Once informed, it starts at a random position on the list, but from then on informs its neighbors in the order of the list. Surprisingly, irrespective of the orders of the lists, the above-mentioned bounds still hold. In some cases, even better bounds than for the classical model can be shown.

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Section: Results Obtained After This Workmentioning

confidence: 99%

Quasirandom Rumor Spreading

Doerr

Friedrich

Sauerwald

2014

ACM Trans. Algorithms

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“…The widely-used push/pull/push-pull models [21] were studied on several graph classes like the complete graph [27,38], hypercubes and Erdős-Renyi-graphs [24,28] random geometric graphs [26], and preferential-attachment graphs [16]. A list-based quasi-randomized algorithm has been studied in [4,17]. They have in common that they focus on broadcasting a message from a fixed source on a (possibly random but) fixed graph to all nodes.…”

Section: Related Workmentioning

confidence: 99%

Linear-Time Data Dissemination in Dynamic Networks

Schwarz,

Zeiner,

Schmid

2017

Preprint

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Broadcasting and convergecasting are pivotal services in distributed systems, in particular, in wireless ad-hoc and sensor networks, which are characterized by timevarying communication graphs. We study the question of whether it is possible to disseminate data available locally at some process to all n processes in sparsely connected synchronous dynamic networks with directed links in linear time. Recently, Charron-Bost, Függer and Nowak proved an upper bound of O(n log n) rounds for the case where every communication graph is an arbitrary directed rooted tree. We present a new formalism, which not only facilitates a concise proof of this result, but also allows us to prove that O(n) data dissemination is possible when the number of leaves of the rooted trees are bounded by a constant. In the special case of rooted chains, only (n − 1) rounds are needed. Our approach can also be adapted for undirected networks, where only (n − 1)/2 rounds in the case of arbitrary chain graphs are needed. * This work has been supported by the Austrian Science Fund (FWF) projects ADynNet (P28182) and RiSE (S11405).

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