We study the k-wise independent relaxation of the usual model G(N,p) of random graphs where, as in this model, N labeled vertices are fixed and each edge is drawn with probability p, however, it is only required that the distribution of any subset of k edges is independent.This relaxation can be relevant in modeling phenomena where only k-wise independence is assumed to hold, and is also useful when the relevant graphs are so huge that handling G(N,p) graphs becomes infeasible, and cheaper random-looking distributions (such as k-wise independent ones) must be used instead. Unfortunately, many well-known properties of random graphs in G(N,p) are global, and it is thus not clear if they are guaranteed to hold in the k-wise independent case. We explore the properties of k-wise independent graphs by providing upper-bounds and lower-bounds on the amount of independence, k, required for maintaining the main properties of G(N,p) graphs: connectivity, Hamiltonicity, the connectivity-number, clique-number and chromatic-number and the appearance of fixed subgraphs. Most of these properties are shown to be captured by either constant k or by some k=poly(log(N)) for a wide range of values of p, implying that random looking graphs on N vertices can be generated by a seed of size poly(log(N)). The proofs combine combinatorial, probabilistic and spectral techniques.

We initiate a general study of the feasibility of implementing (huge) random objects, and demonstrate its applicability to a number of areas in which random objects occur naturally. We highlight two types of measures of the quality of the implementation (with respect to the desired specification): The first type corresponds to various standard notions of indistinguishability (applied to function ensembles), whereas the second type is a novel notion that we call truthfulness. Intuitively, a truthful implementation of a random object of Type T must (always) be an object of Type T, and not merely be indistinguishable from a random object of Type T. Our formalism allows for the consideration of random objects that satisfy some fixed property (or have some fixed structure) as well as the consideration of objects supporting complex queries. For example, we consider the truthful implementation of random Hamiltonian graphs as well as supporting complex queries regarding such graphs (e.g., providing the next vertex along a fixed Hamiltonian path in such a graph).