Recent advances in scalable network generation
Random graph models are frequently used as a controllable and versatile data source for
experimental campaigns in various research fields. Generating such data-sets at scale is a …
experimental campaigns in various research fields. Generating such data-sets at scale is a …
The hyperkron graph model for higher-order features
In this manuscript we present the HyperKron Graph model: an extension of the Kronecker
Model, but with a distribution over hyperedges. We prove that we can efficiently generate …
Model, but with a distribution over hyperedges. We prove that we can efficiently generate …
Sampling of attributed networks from hierarchical generative models
Network sampling is a widely used procedure in social network analysis where a random
network is sampled from a generative network model (GNM). Recently proposed GNMs …
network is sampled from a generative network model (GNM). Recently proposed GNMs …
Tied Kronecker product graph models to capture variance in network populations
Much of the past work on mining and modeling networks has focused on understanding the
observed properties of single example graphs. However, in many real-life applications it is …
observed properties of single example graphs. However, in many real-life applications it is …
Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities
Common models for random graphs, such as Erdös--Rényi and Kronecker graphs,
correspond to generating random adjacency matrices where each entry is nonzero based …
correspond to generating random adjacency matrices where each entry is nonzero based …
Scalable and exact sampling method for probabilistic generative graph models
Interest in modeling complex networks has fueled the development of multiple probabilistic
generative graph models (PGGMs). PGGMs are statistical methods that model the network …
generative graph models (PGGMs). PGGMs are statistical methods that model the network …
Motifs, coherent configurations and second order network generation
JC Bronski, T Ferguson - Physica D: Nonlinear Phenomena, 2022 - Elsevier
In this paper we illuminate some algebraic-combinatorial structure underlying the second
order networks (SONETS) random graph model of Zhao, Beverlin, Netoff and Nykamp and …
order networks (SONETS) random graph model of Zhao, Beverlin, Netoff and Nykamp and …
Recent advances in scalable network generation 1
334Random graph models are frequently used as a controllable and versatile data source
for experimental campaigns in various research fields. Generating such data-sets at scale is …
for experimental campaigns in various research fields. Generating such data-sets at scale is …
Scalable generation of random graphs
M Penschuck - 2020 - publikationen.ub.uni-frankfurt.de
Netzwerkmodelle spielen in verschiedenen Wissenschaftsdisziplinen eine wichtige Rolle
und dienen unter anderem der Beschreibung realistischer Graphen. Sie werden häufig als …
und dienen unter anderem der Beschreibung realistischer Graphen. Sie werden häufig als …
Using Bayesian network representations for effective sampling from generative network models
Bayesian networks (BNs) are used for inference and sampling by exploiting conditional
independence among random variables. Context specific independence (CSI) is a property …
independence among random variables. Context specific independence (CSI) is a property …