HyGen: generating random graphs with hyperbolic communities

Generating Random Hyperbolic Graphs in Subquadratic Time

Complex networks have become increasingly popular for modeling various real-world phenomena. Realistic generative network models are important in this context as they simplify complex network research regarding data sharing, reproducibility, and scalability studies. Random hyperbolic graphs are a very promising family of geometric graphs with unit-disk neighborhood in the hyperbolic plane. Prev...

Generating Random Outerplanar Graphs

Generating Practical Random Hyperbolic Graphs in Near-Linear Time and with Sub-Linear Memory

Random graph models, originally conceived to study the structure of networks and the emergence of their properties [8], have become an indispensable tool for experimental algorithmics. Amongst them, hyperbolic random graphs form a well-accepted family, yielding realistic complex networks while being both mathematically and algorithmically tractable. We introduce two generators MemGen and HyperG...

Generating random graphs with large girth

We present a simple and efficient algorithm for randomly generating simple graphs without small cycles. These graphs can be used to design high performance Low-Density Parity-Check (LDPC) codes. For any constant k, α ≤ 1/2k(k + 3) and m = O(n), our algorithm generates an asymptotically uniform random graph with n vertices, m edges, and girth larger than k in polynomial time. To the best of our ...

Generating Random Graphs with Tunable Clustering Coefficient