Revisiting Gossip-Based Ad-Hoc Routing

Gaba, Albana; Voulgaris, Spyros; Iwanicki, Konrad; Steen, Maarten van

doi:10.1109/icccn.2012.6289312

Cited by 8 publications

(5 citation statements)

References 12 publications

(14 reference statements)

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“…In future, theoretical studies on cascades size distribution could explain phenomena in Gossip-based routing [10], rumor virality and influence maximization [14], recurrence of the cascades [7] or assist in forecasting rumors [15]. Currently the research in this area is purely empirical and we need more theoretical models to understand the process of information propagation [20].…”

Section: Related Workmentioning

confidence: 99%

Why Do Cascade Sizes Follow a Power-Law?

Węgrzycki

Sankowski

Pacuk

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.

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Section: Related Workmentioning

confidence: 99%

Why Do Cascade Sizes Follow a Power-Law?

Węgrzycki

Sankowski

Pacuk

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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“…Today, when more and more communication can be tracked and logged, those fields of research can benefit from the analysis of the social networks. The research on the information diffusion can be applied to maximize the influence [12] and virality of the rumor, enhancing recommender systems or improving routing algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

There is Something Beyond the Twitter Network

Pacuk

Sankowski

Węgrzycki

et al. 2016

Proceedings of the 27th ACM Conference on Hypertext and Social Media

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How information spreads through a social network? Can we assume, that the information is spread only through a given social network graph? What is the correct way to compare the models of information flow? These are the basic questions we address in this work. We focus on meticulous comparison of various, well-known models of rumor propagation in the social network. We introduce the model incorporating mass media and effects of absent nodes. In this model the information appears spontaneously in the graph. Using the most conservative metric, we showed that the distribution of cascades sizes generated by this model fits the real data much better than the previously considered models.Comment: 8 pages, 2 figures, Hypertext 201

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“…In recent years, a variety of network forms with obvious self-organizing characteristics, such as P2P, Grid, Wireless Sensor Network (WSN), ad hoc [1], and cloud [2], have been developing and evolving. Self-organizing significantly improves organization and management of nodes in large-scale distributed systems.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, a variety of large-scale distributed systems, such as P2P, grid, WSN, ad hoc [1], and cloud computing system [2], have been developing rapidly. For these systems, fault detection is valuable for system management, replication, load balancing, and other distributed services [3].…”

Section: Introductionmentioning

confidence: 99%