Many peer-assisted content-distribution systems reward a peer based on the amount of data that this peer serves to others. However, validating that a peer did so is, to our knowledge, an open problem; e.g., a group of colluding attackers can earn rewards by claiming to have served content to one another, when they have not. We propose a puzzle mechanism to make contribution-aware peer-assisted ...

We analyze special random network models – so-called thickened trees – which are constructed by random trees where the nodes are replaced by local clusters. These objects serve as model for random real world networks. It is shown that under a symmetry condition for the cluster sets a local-global principle for the degree distribution holds: the degrees given locally through the choice of the cl...

We apply a network complexity measure to the gap junction network of the somatic nervous system of C. elegans and find that it possesses a much higher complexity than we might expect from its degree distribution alone. This “excess” complexity is seen to be caused by a relatively small set of connections involving command interneurons. We describe a method which progressively deletes these “com...

We investigate a class of random graph ensembles based on the Feynman graphs of multidimensional integrals, representing statistical-mechanical partition functions. We show that the resulting ensembles of random graphs strongly resemble those defined in random graphs with hidden color, generalizing the known relation of the Feynman graphs of simple one-dimensional integrals to random graphs wit...

We introduce and study a class of exchangeable random graph ensembles. They can be used as statistical null models for empirical networks, and as a tool for theoretical investigations. We provide general theorems that characterize the degree distribution of the ensemble graphs, together with some features that are important for applications, such as subgraph distributions and kernel of the adja...

For β = 1 the Weibull distribution coincides with the exponential distribution with mean α. In general, α represents the .632-quantile of the Weibull distribution regardless of the value of β since Fα,β(α) = 1 − exp(−1) ≈ .632 for all β > 0. Figure 1 shows a representative collection of Weibull densities. Note that the spread of the Weibull distributions around α gets smaller as β increases. Th...

The random graph model has recently been extended to a random preferential attachment graph model, in order to enable the study of general asymptotic properties in network types that are better represented by the preferential attachment evolution model than by the ordinary (uniform) evolution lodel. Analogously, this paper extends the random hypergraph model to a random preferential attachment ...