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      On \(k\)-connectivity and minimum vertex degree in random \(s\)-intersection graphs

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          Abstract

          Random \(s\)-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random \(s\)-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least \(s\) items (the graph is called a random intersection graph when \(s=1\)). A special case of particular interest is a uniform random \(s\)-intersection graph, where each vertex independently selects the same number of items uniformly at random from a common item pool. Another important case is a binomial random \(s\)-intersection graph, where each item from a pool is independently assigned to each vertex with the same probability. Both models have found numerous applications thus far including cryptanalysis, and the modeling of recommender systems, secure sensor networks, online social networks, trust networks and small-world networks (uniform random \(s\)-intersection graphs), as well as clustering analysis, classification, and the design of integrated circuits (binomial random \(s\)-intersection graphs). In this paper, for binomial/uniform random \(s\)-intersection graphs, we present results related to \(k\)-connectivity and minimum vertex degree. Specifically, we derive the asymptotically exact probabilities and zero-one laws for the following three properties: (i) \(k\)-vertex-connectivity, (ii) \(k\)-edge-connectivity and (iii) the property of minimum vertex degree being at least \(k\).

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          A key-management scheme for distributed sensor networks

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            Degree and clustering coefficient in sparse random intersection graphs

            We establish asymptotic vertex degree distribution and examine its relation to the clustering coefficient in two popular random intersection graph models of Godehardt and Jaworski [Electron. Notes Discrete Math. 10 (2001) 129-132]. For sparse graphs with a positive clustering coefficient, we examine statistical dependence between the (local) clustering coefficient and the degree. Our results are mathematically rigorous. They are consistent with the empirical observation of Foudalis et al. [In Algorithms and Models for Web Graph (2011) Springer] that, "clustering correlates negatively with degree." Moreover, they explain empirical results on \(k^{-1}\) scaling of the local clustering coefficient of a vertex of degree k reported in Ravasz and Barabasi [Phys. Rev. E 67 (2003) 026112].
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              A lower-bound on the number of rankings required in recommender systems using collaborativ filtering

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                Author and article information

                Journal
                2014-09-21
                2014-11-18
                Article
                1409.6021
                08a84ed7-773d-4b90-be11-02057490950f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                05C80, 60B20
                This paper appears in ACM-SIAM Meeting on Analytic Algorithmics and Combinatorics (ANALCO) 2015, a conference co-located with ACM-SIAM Symposium on Discrete Algorithms (SODA) 2015
                math.CO cs.DM cs.SI math.PR physics.soc-ph

                Social & Information networks,General physics,Combinatorics,Discrete mathematics & Graph theory,Probability

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