Approximation Algorithms in Graphs with Known Broadcast Time of the Base Graph

Bhabak, Puspal; Harutyunyan, Hovhannes A.

doi:10.1007/978-3-030-95018-7_24

Cited by 3 publications

(2 citation statements)

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“…Finally, there is a long list of heuristics and approximation algorithms for this problem. Several heuristic [25], [32]- [35] and approximation algorithms for arbitrary graphs [36]- [38], or a specific family of graphs [39], [40] have been proposed in the literature. The algorithm with the best approximation ratio is proposed in [41] which guarantees a ( log |V | log log |V | )approximation for this problem.…”

Section: A Classical Broadcastingmentioning

confidence: 99%

“…The reason for defining this function is quite obvious. In recent years, finding the broadcast time of a particular family of graphs has been an ongoing research question [20], [30], [39], [40], particularly under the universal list model [10], [45]- [48]. We claim that using the proposed algorithm, we can tackle this problem in two different ways: Firstly, our algorithm generates the actual broadcast scheme that minimizes its fitness function.…”

Section: B Fitness Functionmentioning

confidence: 99%

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HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Gholami

Harutyunyan

2023

J. Commun. Netw.

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Section: A Classical Broadcastingmentioning

confidence: 99%

Section: B Fitness Functionmentioning

confidence: 99%

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Gholami

Harutyunyan

2023

J. Commun. Netw.

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Fully-adaptive Model for Broadcasting with Universal Lists

Gholami

Harutyunyan

2022

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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A Note to Non-adaptive Broadcasting

Gholami,

Harutyunyan

2024

Parallel Process. Lett.

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Broadcasting is a fundamental problem in the information dissemination area. In classical broadcasting, a message must be sent from one network member to all other members as rapidly as feasible. Although it has been demonstrated that this problem is NP-Hard for arbitrary graphs, it has several applications in various fields. As a result, the universal lists model, replicating real-world restrictions like the memory limits of nodes in large networks, is introduced as a branch of this problem in the literature. In the universal lists model, each node is equipped with a fixed list and has to follow the list regardless of the originator. In this study, we focus on the non-adaptive branch of universal lists broadcasting. In this regard, we establish the optimal broadcast time of [Formula: see text]-ary trees and binomial trees under the non-adaptive model and provide an upper bound for complete bipartite graphs. We also improved a general upper bound for trees under the same model and showed that our upper bound cannot be improved in general.

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Approximation Algorithms in Graphs with Known Broadcast Time of the Base Graph

Cited by 3 publications

References 20 publications

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Fully-adaptive Model for Broadcasting with Universal Lists

A Note to Non-adaptive Broadcasting

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