scholarly journals Analysis of the formation of the structure of social networks by using latent space models for ranked dynamic networks

2015 ◽  
Vol 64 (4) ◽  
pp. 611-633 ◽  
Author(s):  
Daniel K. Sewell ◽  
Yuguo Chen
Keyword(s):  
Social Networks ◽  
Dynamic Networks ◽  
Latent Space ◽  
Latent Space Models

scholarly journals Latent space models for dynamic networks with weighted edges

2016 ◽  
Vol 44 ◽  
pp. 105-116 ◽  
Author(s):  
Daniel K. Sewell ◽  
Yuguo Chen
Keyword(s):  
Dynamic Networks ◽  
Latent Space ◽  
Latent Space Models

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

scholarly journals Latent Space Models for Dynamic Networks

2015 ◽  
Vol 110 (512) ◽  
pp. 1646-1657 ◽  
Author(s):  
Daniel K. Sewell ◽  
Yuguo Chen
Keyword(s):  
Dynamic Networks ◽  
Latent Space ◽  
Latent Space Models

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 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 Latent Space Approaches to Community Detection in Dynamic Networks

2017 ◽  
Vol 12 (2) ◽  
pp. 351-377 ◽  
Author(s):  
Daniel K. Sewell ◽  
Yuguo Chen
Keyword(s):  
Community Detection ◽  
Dynamic Networks ◽  
Latent Space

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.

Sign in / Sign up

Export Citation Format

Share Document