LAUSR

Random graph generators are necessary tools for many network science applications. For example, the evaluation of graph analysis algorithms requires methods for generating realistic synthetic graphs. Typically random graph generators are generating graphs that satisfy certain global criteria, such as degree distribution or diameter. If the generated graph is to be used to evaluate community detection and mining algorithms, however, the generator must produce realistic community structure, as well. Recent research has shown that a clique is not necessarily a realistic community structure, necessitating the development of new graph generators. We propose HyGen, a random graph generator that leverages the recent research on non-clique-like communities to produce realistic random graphs with hyperbolic community structure, degree distribution, and clustering coefficient. Our generator can also be used to accurately model time-evolving communities.