A latent space model for cognitive social structures data

Sosa, Juan; Rodríguez, Abel

doi:10.1016/j.socnet.2020.12.002

Cited by 8 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research on emotional modeling and application has been paid extensive attention [ 17 , 18 ]. Especially, literature [ 19 , 20 ] puts forward the emotional cognitive learning and decision-making model by applying Boltzmann selection mechanism, which takes emotional factors as internal influences and external environment as external influences, and realizes emotional cognitive learning and decision-making by calculating the internal and external influence probabilities after decision-making; Sosa and Rodríguez [ 21 ] realized the coordinated control of robot behavior on the basis of emotional cognitive learning and decision-making model. However, the application of emotional model was limited because it adopted symbolic cognitive state and decision-making behavior.…”

Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

show abstract

Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…This model for multilayer network data is such that the resulting joint marginal distribution of the data is fully jointly exchangeable, which means that the joint distribution of {y i,i ,j } is the same as the distribution of {y π 1 (i),π 2 (i ),π 3 (j) } only if π 1 = π 2 , where π 1 and π 2 are permutations of [I], and π 3 is a permutation of [J]. Full joint exchangeability (rather than a weaker form of exchangeability) is particularly attractive in this setting because all indexes i, i (and potentially j) refer to the same set of actors (Sosa and Rodriguez, 2021).…”

Section: Extension To Multilayer Networkmentioning

confidence: 99%

“…In this section we develop a formal test to assess the level of "agreement" (as opposed to "accuracy", which requires the definition of an external "gold standard"), between an actor's self perception of their own position in a social environment and that of other actors embedded in the same system (e.g., Swartz et al, 2015, Sewell, 2019, Sosa and Rodriguez, 2021. Our approach relies on the hierarchical structure of the MNLPM, which allow us to define a measure of cognitive agreement.…”

Section: Friendship Datamentioning

confidence: 99%

“…Additional work related to cross-sectional multilayer network data includes community detection (e.g., Han et al, 2015, Reyes and Rodriguez, 2016, Paul and Chen, 2016, Gao et al, 2019, Paez et al, 2019, and Paul et al, 2020, and the perception assessment in cognitive social structures (e.g., Swartz et al, 2015, Sewell, 2019, and Sosa and Rodriguez, 2021. Finally, from the dynamic point of view, there is a large variety of approaches to modeling network evolution over time (e.g., Durante and Dunson, 2014, Hoff, 2015, Sewell and Chen, 2015, Gupta et al, 2018, Kim et al, 2018, Turnbull, 2020, Betancourt et al, 2020.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Latent Space Model for Multilayer Network Data

Sosa¹,

Betancourt²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

In this work, we propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification easily allows us to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgements that subjects form about the relationships among actors, and perform clustering tasks at different social instances. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.

show abstract

“…In particular, we attempt to match a Beta(0.5, 0.5) distribution, which is both the reference and the Jeffreys prior for the Bernoulli likelihood in the fully exchangeable case (Berger, Bernardo, & Sun, 2009;Bernardo, 1979). A similar approach to prior elicitation in the context of latent space models for networks can be found in Guhaniyogi and Rodriguez (2020) and Sosa and Rodrìguez (2021).…”

Section: Choice Of Prior Distributionsmentioning

confidence: 99%

Computational methods for Bayesian semiparametric Item Response Theory models

Paganin¹,

Paciorek²,

Wehrhahn³

et al. 2021

Preprint

View full text Add to dashboard Cite

Item response theory (IRT) models are widely used to obtain interpretable inference when analyzing data from questionnaires, scaling binary responses into continuous constructs. Typically, these models rely on a normality assumption for the latent trait characterizing individuals in the population under study. However, this assumption can be unrealistic and lead to biased results. We relax the normality assumption by considering a flexible Dirichlet Process mixture model as a nonparametric prior on the distribution of the individual latent traits. Although this approach has been considered in the literature before, there is a lack of comprehensive studies of such models or general software tools. To fill this gap, we show how the NIMBLE framework for hierarchical statistical modeling enables the use of flexible priors on the latent trait distribution, specifically illustrating the use of Dirichlet Process mixtures in two-parameter logistic (2PL) IRT models. We study how different sets of constraints can lead to model identifiability and give guidance on eliciting prior distributions. Using both simulated and real-world data, we conduct an in-depth study of Markov chain Monte Carlo posterior sampling efficiency for several sampling strategies. These strategies consider various combinations of model parameterizations, identifiability constraints and sampling algorithms. In the simulated scenarios, we find that some choices of parameterization and identifiability constraints can yield order-of-magnitude differences in sampling efficiency compared to others. We also find that there is little inferential penalty in using

show abstract

A latent space model for cognitive social structures data

Cited by 8 publications

References 34 publications

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

A Latent Space Model for Multilayer Network Data

Computational methods for Bayesian semiparametric Item Response Theory models

Contact Info

Product

Resources

About