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N-partite networks are natural representations of complex multientity databases. However, processing these networks can be a highly memory and computation-intensive task, especially when positive correlation exists between the degrees of vertices from different partitions. In order to improve the scalability of this process, this paper proposes two algorithms that make use of sampling for obtaining less expensive approximate results. The first algorithm is optimal for obtaining homogeneous discovery rates with a low memory requirement, but can be very slow in cases where the combined branching factor of these networks is too large. A second algorithm that incorporates concepts from evolutionary computation aims toward dealing with this slow convergence in the case when it is more interesting to increase approximation convergence speed of elements with high feature values. This algorithm makes use of the positive correlation between "local" branching factors and the feature values. Two applications examples are demonstrated in searching for most influential authors in collections of journal articles and in analyzing most active earthquake regions from a collection of earthquake events.