Graphs and networks are a natural data representation for analysis of a myriad of domains, ranging from systems analysis (e.g., the Internet) to bioinformatics (e.g., protein interactions) to psycho-social domains (e.g., online socia
Graph and network models are essential for data science applications in computer science, social sci...
We examine the marriage of recent probabilistic generative models for social networks with classical...
Abstract. Graphs are useful data structures capable of efficiently representing a variety of technol...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
A complex network is an abstract representation of an intricate system of interrelated elements wher...
network consists of a collection of nodes and a collection of connections that join pairs of nodes. ...
Statistical models of networks are widely used to reason about the properties of complex systems—whe...
The scientific study of networks, including computer networks, social networks, and biological netwo...
In this chapter, we survey graph mining methods. We focus on graph pattern mining, but also discuss...
International audienceDesigning plausible network models typically requires scholars to form a prior...
Mehler A. A Quantitative Graph Model of Social Ontologies by Example of Wikipedia. In: Dehmer M, Emm...
AbstractDuring the past several years, social network analysis methods have been used to model many ...
Mathematical problems such as graph theory problems are of increasing importance for the analysis of...
A method for the reliable generation of random networks that model known social networks is becoming...
Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, fr...
Graph and network models are essential for data science applications in computer science, social sci...
We examine the marriage of recent probabilistic generative models for social networks with classical...
Abstract. Graphs are useful data structures capable of efficiently representing a variety of technol...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
A complex network is an abstract representation of an intricate system of interrelated elements wher...
network consists of a collection of nodes and a collection of connections that join pairs of nodes. ...
Statistical models of networks are widely used to reason about the properties of complex systems—whe...
The scientific study of networks, including computer networks, social networks, and biological netwo...
In this chapter, we survey graph mining methods. We focus on graph pattern mining, but also discuss...
International audienceDesigning plausible network models typically requires scholars to form a prior...
Mehler A. A Quantitative Graph Model of Social Ontologies by Example of Wikipedia. In: Dehmer M, Emm...
AbstractDuring the past several years, social network analysis methods have been used to model many ...
Mathematical problems such as graph theory problems are of increasing importance for the analysis of...
A method for the reliable generation of random networks that model known social networks is becoming...
Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, fr...
Graph and network models are essential for data science applications in computer science, social sci...
We examine the marriage of recent probabilistic generative models for social networks with classical...
Abstract. Graphs are useful data structures capable of efficiently representing a variety of technol...