The recent explosion in social network data has stimulated interest in probabilistic models of networks. Such models are appealing because they are empirically grounded; in con-trast to more traditional network models, their parameter
We propose a family of statistical models for social network evolution over time, which represents ...
Abstract. The study of social networks has gained new importance with the recent rise of large on-li...
The science of social networks is a central \u85eld of sociological study, a major appli-cation of r...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
Modeling the evolution of networks is central to our understanding of large communication systems, a...
We study a dynamic model of network formation introduced by Matthew Jackson and Brian Rogers in the ...
Why model networks? Many datasets take the form of networks or graphs... Social networks have binary...
Abstract. The study of social networks has gained new importance with the recent rise of large on-li...
This rigorous introduction to network science presents random graphs as models for real-world networ...
Abstract. Several techniques for learning statistical models have been developed recently by researc...
Abstract: We propose a new model of social network growth. This model bases on the followi...
We propose a family of statistical models for social network evolution over time, which represents a...
Social network formation models are often compared by their network structures, which satisfy specif...
Statistical models of networks are widely used to reason about the properties of complex systems—whe...
Current Bayesian models for dynamic social network data have focused on modelling the influence of e...
We propose a family of statistical models for social network evolution over time, which represents ...
Abstract. The study of social networks has gained new importance with the recent rise of large on-li...
The science of social networks is a central \u85eld of sociological study, a major appli-cation of r...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
Modeling the evolution of networks is central to our understanding of large communication systems, a...
We study a dynamic model of network formation introduced by Matthew Jackson and Brian Rogers in the ...
Why model networks? Many datasets take the form of networks or graphs... Social networks have binary...
Abstract. The study of social networks has gained new importance with the recent rise of large on-li...
This rigorous introduction to network science presents random graphs as models for real-world networ...
Abstract. Several techniques for learning statistical models have been developed recently by researc...
Abstract: We propose a new model of social network growth. This model bases on the followi...
We propose a family of statistical models for social network evolution over time, which represents a...
Social network formation models are often compared by their network structures, which satisfy specif...
Statistical models of networks are widely used to reason about the properties of complex systems—whe...
Current Bayesian models for dynamic social network data have focused on modelling the influence of e...
We propose a family of statistical models for social network evolution over time, which represents ...
Abstract. The study of social networks has gained new importance with the recent rise of large on-li...
The science of social networks is a central \u85eld of sociological study, a major appli-cation of r...