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We present a nearly-linear time algorithm for counting and randomly generating simple graphs with a given degree sequence in a certain range. For degree sequence $(d_i)_{i=1}^n$ with maximum degree $d_{max}=O(m^{1/4-tau})$, our algorithm generates almost uniform random graphs with that degree sequence in time $O(m,d_{max})$ where $m=f{1}{2}sum_id_i$ is the number of edges in the graph and $tau$ is any positive constant. The fastest known algorithm for uniform generation of these graphs McKay Wormald (1990) has a running time of $O(m^2d_{max}^2)$. Our method also gives an independent proof of McKays estimate McKay (1985) for the number of such graphs. We also use sequential importance sampling to derive fully Polynomial-time Randomized Approximation Schemes (FPRAS) for counting and uniformly generating random graphs for the same range of $d_{max}=O(m^{1/4-tau})$. Moreover, we show that for $d = O(n^{1/2-tau})$, our algorithm can generate an asymptotically uniform $d$-regular graph. Our results improve the previous bound of $d = O(n^{1/3-tau})$ due to Kim and Vu (2004) for regular graphs.
Best match graphs (BMGs) are vertex-colored directed graphs that were introduced to model the relationships of genes (vertices) from different species (colors) given an underlying evolutionary tree that is assumed to be unknown. In real-life applicat
Common models for random graphs, such as ErdH{o}s-R{e}nyi and Kronecker graphs, correspond to generating random adjacency matrices where each entry is non-zero based on a large matrix of probabilities. Generating an instance of a random graph based o
The isomorphism problem is known to be efficiently solvable for interval graphs, while for the larger class of circular-arc graphs its complexity status stays open. We consider the intermediate class of intersection graphs for families of circular ar
The first-fit coloring is a heuristic that assigns to each vertex, arriving in a specified order $sigma$, the smallest available color. The problem Grundy Coloring asks how many colors are needed for the most adversarial vertex ordering $sigma$, i.e.
We consider bootstrap percolation and diffusion in sparse random graphs with fixed degrees, constructed by configuration model. Every node has two states: it is either active or inactive. We assume that to each node is assigned a nonnegative (integer