Constructing Visual Models with a Latent Space Approach

Interfirm relationships are crucial to our understanding of firms’ collective and interactive behavior. Many information systems-related phenomena, including the diffusion of innovations, standard alliances, technology collaboration, and outsourcing, involve a multitude of relationships between firms. This study proposes a latent space approach to model temporal change in a dual-view interfirm network. We assume that interfirm relationships depend on an underlying latent space; firms that are close to each other in the latent space are more likely to develop a relationship. We construct the latent space by embedding two dynamic networks of firms in an integrated manner, resulting in a more comprehensive view of an interfirm relationship. We validate our approach by introducing three business measures derived from the latent space model to study alliance formation and stock comovement. We illustrate how the trajectories of firms provide insights into alliance activities. We also show that our proposed measures have strong predictive power on stock comovement. We believe the proposed approach enriches the methodology toolbox of IS researchers in studying interfirm relationships.

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Improved latent space approach for modelling non-stationary spatial–temporal random fields

Spatial Statistics ◽

10.1016/j.spasta.2018.01.003 ◽

2018 ◽

Vol 23 ◽

pp. 160-181 ◽

Cited By ~ 8

Author(s):

Hao Xu ◽

Paolo Gardoni

Keyword(s):

Random Fields ◽

Latent Space ◽

Space Approach

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Who are in charge, who do i work with, and who are my friends: A latent space approach to understanding elite coappearances in China

Social Networks ◽

10.1016/j.socnet.2021.01.002 ◽

2021 ◽

Vol 66 ◽

pp. 26-37

Author(s):

Narisong Huhe ◽

Max Gallop ◽

Shahryar Minhas

Keyword(s):

Latent Space ◽

Space Approach

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Modelling interactions among offenders: A latent space approach for interdependent ego-networks

Social Networks ◽

10.1016/j.socnet.2020.07.001 ◽

2020 ◽

Vol 63 ◽

pp. 134-149

Author(s):

Isabella Gollini ◽

Alberto Caimo ◽

Paolo Campana

Keyword(s):

Ego Networks ◽

Latent Space ◽

Space Approach

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A Generative Latent Space Approach for Real-time Road Surveillance in Smart Cities

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3037286 ◽

2020 ◽

pp. 1-1

Author(s):

Rashmika Nawaratne ◽

Sachin Kahawala ◽

Su Nguyen ◽

Daswin De Silva

Keyword(s):

Real Time ◽

Smart Cities ◽

Latent Space ◽

Space Approach

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Computer Assisted Image Reconstruction of Oligomer Structure

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092323 ◽

1976 ◽

Vol 34 ◽

pp. 472-473

Author(s):

A.M. Jones ◽

A. Max Fiskin

Keyword(s):

Three Dimensional ◽

Depth Of Field ◽

Computer Assisted ◽

Projection Data ◽

Two Dimensional ◽

Single Particles ◽

Dimensional Reconstruction ◽

Computer Assisted Image ◽

The Fourier Transform ◽

Space Approach

If the tilt of a specimen can be varied either by the strategy of observing identical particles orientated randomly or by use of a eucentric goniometer stage, three dimensional reconstruction procedures are available (l). If the specimens, such as small protein aggregates, lack periodicity, direct space methods compete favorably in ease of implementation with reconstruction by the Fourier (transform) space approach (2). Regardless of method, reconstruction is possible because useful specimen thicknesses are always much less than the depth of field in an electron microscope. Thus electron images record the amount of stain in columns of the object normal to the recording plates. For single particles, practical considerations dictate that the specimen be tilted precisely about a single axis. In so doing a reconstructed image is achieved serially from two-dimensional sections which in turn are generated by a series of back-to-front lines of projection data.

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The Dark Tetrad

Journal of Individual Differences ◽

10.1027/1614-0001/a000179 ◽

2015 ◽

Vol 36 (4) ◽

pp. 228-236 ◽

Cited By ~ 45

Author(s):

Janko Međedović ◽

Boban Petrović

Keyword(s):

Personality Traits ◽

Antisocial Behavior ◽

Second Order ◽

The Other ◽

Negative Pole ◽

Latent Space ◽

Order Factor

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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