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2.5K-Graphs: from Sampling to Generation
Understanding network structure and having access to realistic graphs plays a
central role in computer and social networks research. In this paper, we
propose a complete, and practical methodology for generating graphs that
resemble a real graph of interest. The metrics of the original topology we
target to match are the joint degree distribution (JDD) and the
degree-dependent average clustering coefficient (). We start by
developing efficient estimators for these two metrics based on a node sample
collected via either independence sampling or random walks. Then, we process
the output of the estimators to ensure that the target properties are
realizable. Finally, we propose an efficient algorithm for generating
topologies that have the exact target JDD and a close to the
target. Extensive simulations using real-life graphs show that the graphs
generated by our methodology are similar to the original graph with respect to,
not only the two target metrics, but also a wide range of other topological
metrics; furthermore, our generator is order of magnitudes faster than
state-of-the-art techniques
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