In social network studies, most often only a single relation (or link) between the actors is investigated. When more than one link has been recorded, the two-way sociomatrix becomes a three-way array with the set of links being the third way. In this paper, we present a model which simultaneously accounts for the three ways in the data. Random effects are used to model the between-actor variability, both on senders and receivers side. In addition, structural relations between the linking variables are investigated. The model is applied to a study of popularity and strength in a class of students. It is shown that popularity can be seen as a linear function of strength on the receivers’ side, but not on the senders’ side
We consider the specification of effects of numerical actor attributes, having an interval level of ...
The present work is a collection of articles [133, 134, 135] that, in broad terms, are dedicated to...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
A general approach to exploratory analysis and modeling of network data is to investigate dyad distr...
Social networks are usually collected as dyadic data: the relations betweenpairs of actors are recor...
Social networks are usually collected as dyadic data: the relations between pairs of actors are reco...
Dyadic network data occur when each member in a group provides data about each other member in the g...
Modeling dyadic interactions between entities is one of the fundamental problems in machine learning...
A random effects model is proposed for the analysis of binary dyadic data that represent a social ne...
In social network analysis the mean nodal degree and density of the graph can be considered as a mea...
We propose a method for examining and measuring the complexity of animal social networks that are ch...
A primary focus of social network analysis (SNA) is to understand actor attributes from social struc...
We present network models for social selection processes, based on the p* class of models. Social se...
Social relations are multiplex by nature: actors in a group are tied together by various types of re...
The study of networks, sets of objects connected by relationship, is an impor-tant area in sociology...
We consider the specification of effects of numerical actor attributes, having an interval level of ...
The present work is a collection of articles [133, 134, 135] that, in broad terms, are dedicated to...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...
A general approach to exploratory analysis and modeling of network data is to investigate dyad distr...
Social networks are usually collected as dyadic data: the relations betweenpairs of actors are recor...
Social networks are usually collected as dyadic data: the relations between pairs of actors are reco...
Dyadic network data occur when each member in a group provides data about each other member in the g...
Modeling dyadic interactions between entities is one of the fundamental problems in machine learning...
A random effects model is proposed for the analysis of binary dyadic data that represent a social ne...
In social network analysis the mean nodal degree and density of the graph can be considered as a mea...
We propose a method for examining and measuring the complexity of animal social networks that are ch...
A primary focus of social network analysis (SNA) is to understand actor attributes from social struc...
We present network models for social selection processes, based on the p* class of models. Social se...
Social relations are multiplex by nature: actors in a group are tied together by various types of re...
The study of networks, sets of objects connected by relationship, is an impor-tant area in sociology...
We consider the specification of effects of numerical actor attributes, having an interval level of ...
The present work is a collection of articles [133, 134, 135] that, in broad terms, are dedicated to...
Social relationships consist of interactions along multiple dimensions. In social networks, this mea...