Add to ORKGLatent space models (LSM) for network data were introduced by Holf et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected. In this paper we propose a variational inference approach to estimate the intractable posterior of the LSM. In many cases, different network views on the same set of nodes are available. It can therefore be useful to build a model able to jointly summarise the information given by all the network views. For this purpose, we introduce the latent space joint model (LSJM) that merges the information given by multiple net...
Social relationships consist of interactions along multiple dimen-sions. In social networks, this me...
We derive properties of Latent Variable Models for networks, a broad class ofmodels that includes th...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...
Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic ass...
Latent space models (LSM) for network data were introduced by Hoff et al. (2002a) under the basic as...
<p>Latent space models (LSM) for network data rely on the basic assumption that each node of the net...
Network data arises in fields such as neuroimaging, sociology, and medicine, to represent pairwise c...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
The ubiquity of relational data has motivated an extensive literature on network analysis, and over ...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
A number of recent approaches to modeling social networks have focussed on embedding the nodes in a ...
<p>Dynamic networks are used in a variety of fields to represent the structure and evolution of the ...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
Social relationships consist of interactions along multiple dimen-sions. In social networks, this me...
We derive properties of Latent Variable Models for networks, a broad class ofmodels that includes th...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...
Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic ass...
Latent space models (LSM) for network data were introduced by Hoff et al. (2002a) under the basic as...
<p>Latent space models (LSM) for network data rely on the basic assumption that each node of the net...
Network data arises in fields such as neuroimaging, sociology, and medicine, to represent pairwise c...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
The ubiquity of relational data has motivated an extensive literature on network analysis, and over ...
Multidimensional network data can have different levels of complexity, as nodes may be characterized...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
A number of recent approaches to modeling social networks have focussed on embedding the nodes in a ...
<p>Dynamic networks are used in a variety of fields to represent the structure and evolution of the ...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
Social relationships consist of interactions along multiple dimen-sions. In social networks, this me...
We derive properties of Latent Variable Models for networks, a broad class ofmodels that includes th...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...