Latent space models for network perception data

2019 ◽  
Vol 7 (2) ◽  
pp. 160-179
Author(s):  
Daniel K. Sewell
Keyword(s):  
Social Networks ◽  
Network Model ◽  
Social Structures ◽  
Visualization Method ◽  
Space Network ◽  
Statistical Framework ◽  
Potential Respondent ◽  
Latent Space ◽  
Latent Space Models ◽  
Entire Network

AbstractSocial networks, wherein the edges represent nonbehavioral relations such as friendship, power, and influence, can be difficult to measure and model. A powerful tool to address this is cognitive social structures (Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.), where the perception of the entire network is elicited from each actor. We provide a formal statistical framework to analyze informants’ perceptions of the network, implementing a latent space network model that can estimate, e.g., homophilic effects while accounting for informant error. Our model allows researchers to better understand why respondents’ perceptions differ. We also describe how to construct a meaningful single aggregated network that ameliorates potential respondent error. The proposed method provides a visualization method, an estimate of the informants’ biases and variances, and we describe a method for sidestepping forced-choice designs.

scholarly journals Visualizing Hierarchical Social Networks

2018 ◽  
Vol 4 ◽  
pp. 237802311877298 ◽  
Author(s):  
Kurtuluş Gemici ◽  
Anthony Vashevko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Hierarchical Structure ◽  
Social Structures ◽  
Visualization Method ◽  
Novel Technique ◽  
The Hierarchical Structure ◽  
Underlying Network ◽  
Combining Information

The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between groups containing the nodes in the network. As such, the proposed visualization method facilitates theoretical and empirical analysis of social structures by algorithmically combining information from the underlying network with the information from the hierarchical structure of the network. The authors illustrate the proposed method with social networks examined through cohesive blocking and k-core decomposition.

scholarly journals Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood

2012 ◽  
Vol 21 (4) ◽  
pp. 901-919 ◽  
Author(s):  
Adrian E. Raftery ◽  
Xiaoyue Niu ◽  
Peter D. Hoff ◽  
Ka Yee Yeung
Keyword(s):  
Network Model ◽  
Case Control ◽  
Space Network ◽  
Latent Space ◽  
Approximate Likelihood

A Latent Space Network Model for Social Influence

Psychometrika ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. 251-274
Author(s):  
Tracy Sweet ◽  
Samrachana Adhikari
Keyword(s):  
Social Influence ◽  
Network Model ◽  
Space Network ◽  
Latent Space

scholarly journals Penalized homophily latent space models for directed scale-free networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253873
Author(s):  
Hanxuan Yang ◽  
Wei Xiong ◽  
Xueliang Zhang ◽  
Kai Wang ◽  
Maozai Tian
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Distance Measure ◽  
Real Data ◽  
Directed Network ◽  
Finite Sample ◽  
Scale Free ◽  
Latent Space ◽  
Scale Free Networks ◽  
Latent Space Models

Online social networks like Twitter and Facebook are among the most popular sites on the Internet. Most online social networks involve some specific features, including reciprocity, transitivity and degree heterogeneity. Such networks are so called scale-free networks and have drawn lots of attention in research. The aim of this paper is to develop a novel methodology for directed network embedding within the latent space model (LSM) framework. It is known, the link probability between two individuals may increase as the features of each become similar, which is referred to as homophily attributes. To this end, penalized pair-specific attributes, acting as a distance measure, are introduced to provide with more powerful interpretation and improve link prediction accuracy, named penalized homophily latent space models (PHLSM). The proposed models also involve in-degree heterogeneity of directed scale-free networks by embedding with the popularity scales. We also introduce LASSO-based PHLSM to produce an accurate and sparse model for high-dimensional covariates. We make Bayesian inference using MCMC algorithms. The finite sample performance of the proposed models is evaluated by three benchmark simulation datasets and two real data examples. Our methods are competitive and interpretable, they outperform existing approaches for fitting directed networks.

scholarly journals Sublinear domination and core–periphery networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Random Network ◽  
Small World ◽  
The Core ◽  
Entire Network

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

2021 ◽  
Vol 30 (1) ◽  
pp. 19-33
Author(s):  
Annis Shafika Amran ◽  
Sharifah Aida Sheikh Ibrahim ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Nurfaten Hamzah ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Linear Models ◽  
Brain Activity ◽  
Generative Models ◽  
Education Management ◽  
Latent Space ◽  
Latent Space Models ◽  
Cost Feasibility ◽  
Medical Healthcare ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

scholarly journals Cross-cultural similarity in relationship-specific social touching

2019 ◽  
Vol 286 (1901) ◽  
pp. 20190467 ◽  
Author(s):  
Juulia T. Suvilehto ◽  
Lauri Nummenmaa ◽  
Tokiko Harada ◽  
Robin I. M. Dunbar ◽  
Riitta Hari ◽  
...  
Keyword(s):  
Social Networks ◽  
East Asian ◽  
Cross Cultural ◽  
The Body ◽  
Human Studies ◽  
Social Structures ◽  
Social Grooming ◽  
Emotional Bonding ◽  
Social Touch ◽  
Cultural Similarity

Many species use touching for reinforcing social structures, and particularly, non-human primates use social grooming for managing their social networks. However, it is still unclear how social touch contributes to the maintenance and reinforcement of human social networks. Human studies in Western cultures suggest that the body locations where touch is allowed are associated with the strength of the emotional bond between the person touched and the toucher. However, it is unknown to what extent this relationship is culturally universal and generalizes to non-Western cultures. Here, we compared relationship-specific, bodily touch allowance maps across one Western ( N = 386, UK) and one East Asian ( N = 255, Japan) country. In both cultures, the strength of the emotional bond was linearly associated with permissible touch area. However, Western participants experienced social touching as more pleasurable than Asian participants. These results indicate a similarity of emotional bonding via social touch between East Asian and Western cultures.

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