scholarly journals Temporal Latent Space Modeling for Community Prediction

Author(s):  
Hossein Fani ◽  
Ebrahim Bagheri ◽  
Weichang Du
Keyword(s):  
2009 ◽  
Vol 28 (12) ◽  
pp. 1454-1459 ◽  
Author(s):  
Bradley C. Wallet ◽  
Marcilio C. de Matos ◽  
J. Timothy Kwiatkowski ◽  
Yoscel Suarez

2019 ◽  
Vol 56 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Emily Kalah Gade ◽  
Mohammed M Hafez ◽  
Michael Gabbay

Violent conflict among rebels is a common feature of civil wars and insurgencies. Yet, not all rebel groups are equally prone to such infighting. While previous research has focused on the systemic causes of violent conflict within rebel movements, this article explores the factors that affect the risk of conflict between pairs of rebel groups. We generate hypotheses concerning how differences in power, ideology, and state sponsors between rebel groups impact their propensity to clash and test them using data from the Syrian civil war. The data, drawn from hundreds of infighting claims made by rebel groups on social media, are used to construct a network of conflictual ties among 30 rebel groups. The relationship between the observed network structure and the independent variables is evaluated using network analysis metrics and methods including assortativity, community structure, simulation, and latent space modeling. We find strong evidence that ideologically distant groups have a higher propensity for infighting than ideologically proximate ones. We also find support for power asymmetry, meaning that pairs of groups of disparate size are at greater risk of infighting than pairs of equal strength. No support was found for the proposition that sharing state sponsors mitigates rebels’ propensity for infighting. Our results provide an important corrective to prevailing theory, which discounts the role of ideology in militant factional dynamics within fragmented conflicts.


2021 ◽  
Vol 12 (2) ◽  
pp. 1-44
Author(s):  
Ka Chung Ng ◽  
Mike K. P. So ◽  
Kar Yan Tam

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.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Azadeh Rezazadeh Hamedani ◽  
Mohammad Hossein Moattar ◽  
Yahya Forghani

AbstractDissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.


2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović

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.”


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

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.


Sign in / Sign up

Export Citation Format

Share Document