Add to ORKGThe purpose of this article is to introduce a new bipartite graph generation algorithm. Bipartite graphs consist of two types of nodes and edges join only nodes of different types. This data structure appears in various applications (e.g. recommender systems or text clustering). Both real-life datasets and formal tools enable us to evaluate only a limited set of properties of the algorithms that are used in such situations. Therefore, artificial datasets are needed to enhance development and testing of the algorithms. Our generator can be used to produce a wide range of synthetic datasets
AbstractConnected bipartite permutation graphs without vertex labels are investigated. First, the nu...
Because of the huge number of graphs possible even with a small number of nodes, inference on networ...
The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (o...
Abstract. The purpose of this article is to introduce a new iterative random bipartite graph generat...
This book supports researchers who need to generate random networks, or who are interested in the th...
When researching relationships between data entities, the most natural way of presenting them is by ...
A common goal in the network analysis community is the modeling of social network graphs, which tend...
AbstractThe analysis of social networks has received much attention in recent years. Most social str...
National audienceGenerating random graphs which verify a set of predefined properties is a major iss...
We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on t...
In this paper, we review the development of dependence structures for exponential random graph model...
International audienceCurrent models of random graphs do not capture all the properties observed in ...
We consider the problem of modeling complex systems where little or nothing is known about the struc...
Ubiquitous application of computer technology during the last decades has led to the emergence of di...
The Erdos-Renyi (ER) random network model generates graphs under the assumption that there could exi...
AbstractConnected bipartite permutation graphs without vertex labels are investigated. First, the nu...
Because of the huge number of graphs possible even with a small number of nodes, inference on networ...
The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (o...
Abstract. The purpose of this article is to introduce a new iterative random bipartite graph generat...
This book supports researchers who need to generate random networks, or who are interested in the th...
When researching relationships between data entities, the most natural way of presenting them is by ...
A common goal in the network analysis community is the modeling of social network graphs, which tend...
AbstractThe analysis of social networks has received much attention in recent years. Most social str...
National audienceGenerating random graphs which verify a set of predefined properties is a major iss...
We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on t...
In this paper, we review the development of dependence structures for exponential random graph model...
International audienceCurrent models of random graphs do not capture all the properties observed in ...
We consider the problem of modeling complex systems where little or nothing is known about the struc...
Ubiquitous application of computer technology during the last decades has led to the emergence of di...
The Erdos-Renyi (ER) random network model generates graphs under the assumption that there could exi...
AbstractConnected bipartite permutation graphs without vertex labels are investigated. First, the nu...
Because of the huge number of graphs possible even with a small number of nodes, inference on networ...
The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (o...