Survey of DHT topology construction techniques in virtual computing environments

Zhang, Yiming; Lu, Xicheng; Li, Dongsheng

doi:10.1007/s11432-011-4377-6

Cited by 9 publications

(2 citation statements)

References 47 publications

(53 reference statements)

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“…Complex networks have been tremendously utilized to characterize the real world. For instance, the Internet could be treated as a network constituted by routers and physical links among them [1]. However, regarding Facebook, the users could be nodes and the online friendships could be links, which also compose a network.…”

Section: Introductionmentioning

confidence: 99%

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Performance of local information-based link prediction: a sampling perspective

Zhao¹,

Xu²,

Li³

et al. 2012

J. Phys. A: Math. Theor.

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Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained from kinds of sampling methods. Contrarily, in the previous literature, in order to evaluate the performance of the prediction, the known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the diverse sampling approaches beyond. However, different sampling methods might lead to different missing links, especially for the biased ones. For this reason, random partition based evaluation of performance is no longer convincing if we take the sampling method into account. Hence, in this paper, aim at filling this void, we try to reevaluate the performance of local information based link predictions through sampling methods governed division of the training set and the probe set. It is interesting that we find for different sampling methods, each prediction approach performs unevenly. Moreover, most of these predictions perform weakly when the sampling method is biased, which indicates that the performance of these methods is overestimated in the prior works.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, in the real world, the size of the network might be extremely large. For example, Facebook contains nearly 901 million users currently 1 . Because of the large scale, it is indeed very difficult for the research community to get a complete and rich picture of the network.…”

Section: Introductionmentioning

confidence: 99%