2016
DOI: 10.1007/978-3-319-50901-3_9
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Identifying Influential Spreaders by Graph Sampling

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Cited by 5 publications
(15 citation statements)
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“…In this paper, we extend the work in Salamanos et al (2016) , on several levels. First, we devise the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models - a common approach in the literature - in order to define a kind of “ground truth” ranking of the graph nodes with regard to their spreading efficiency.…”
Section: Introductionmentioning
confidence: 88%
See 1 more Smart Citation
“…In this paper, we extend the work in Salamanos et al (2016) , on several levels. First, we devise the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models - a common approach in the literature - in order to define a kind of “ground truth” ranking of the graph nodes with regard to their spreading efficiency.…”
Section: Introductionmentioning
confidence: 88%
“…A first preliminary study which investigates the applications of graph sampling to the influential spreaders identification problem has been conducted in Salamanos et al (2016) , where we studied the effectiveness of Rank Degree as influential spreaders identifier. The Rank Degree is a graph exploration sampling method which can produce representative samples/subgraphs from an unknown graph, using only local information, that is the degree of the visited nodes ( Voudigari et al 2016 ; Salamanos et al 2017 ).…”
Section: Introductionmentioning
confidence: 99%
“…The first version works as outlined above, while with the second version, instead of only the top connected node in Step 1, the top k connected nodes would be selected, where k is calculated by ρ * #connections(w), 0 < ρ ⩽ 1. The first version has been shown to produce better samples in general, and Salamanos et al (2017bSalamanos et al ( , 2017c discuss the algorithm in this form. Comparing the algorithm to other graph sampling methods such as Forest Fire, Frontier Sampling, Metropolis Hastings, and random sampling methods, they find that samples generated by their algorithm preserve several graph properties to a large extent and that influential spreaders can be identified at almost the same accuracy as in the full information case when having explored only 20% of the network.…”
Section: Samplingmentioning
confidence: 99%
“…This method is based on graph sampling, the problem of selecting a small subgraph which has the topological properties as the original graph. A sampling method effectively identifies the influential spreader if and only if (a) the fraction of top-k common nodes in the samples and in the graph is on an average sufficiently large and (b) the ranking of these nodes in the samples are close to the original ranking in the graph [30]. Time complexity of rank degree algorithm is O(n 2 ) for sparse matrix.…”
Section: Rank Degreementioning
confidence: 99%