Affect and Personality

Abstract. The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches – GIMME, uSEM, and LASSO gVAR – in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three dynamic network approaches provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.

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The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

Information ◽

10.3390/info9090212 ◽

2018 ◽

Vol 9 (9) ◽

pp. 212

Author(s):

Zhiwei Yang ◽

Weigang Wu

Keyword(s):

Network Topology ◽

Information Dissemination ◽

Dynamic Networks ◽

Dynamic Network ◽

Network Models ◽

Point Of View ◽

Path Model ◽

Dynamic Network Models ◽

Topology Changes ◽

Practical Design

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

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Factorial graphical models for dynamic networks

Network Science ◽

10.1017/nws.2015.2 ◽

2015 ◽

Vol 3 (1) ◽

pp. 37-57 ◽

Cited By ~ 5

Author(s):

ERNST WIT ◽

ANTONINO ABBRUZZO

Keyword(s):

Graphical Models ◽

Cell Biology ◽

Time Series Data ◽

Dynamic Networks ◽

Real Life ◽

Dynamic Network ◽

Network Models ◽

Series Data ◽

Time Dynamics ◽

Dynamic Network Models

AbstractDynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Until now, the literature has focused on static networks, which lack specific temporal interpretations.We propose a flexible collection of ANOVA-like dynamic network models, where the user can select specific time dynamics, known presence or absence of links, and a particular autoregressive structure. We use undirected graphical models with block equality constraints on the parameters. This reduces the number of parameters, increases the accuracy of the estimates and makes interpretation of the results more relevant. We illustrate the flexibility of the method on both synthetic and real data.

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Cluster-based efficient information dissemination in dynamic networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718762086 ◽

2018 ◽

Vol 14 (3) ◽

pp. 155014771876208

Author(s):

Zhiwei Yang ◽

Weigang Wu ◽

Yong Li ◽

Yishun Chen

Keyword(s):

Information Dissemination ◽

Dynamic Networks ◽

Cluster Head ◽

Dynamic Network ◽

Network Models ◽

Node Mobility ◽

Dynamic Network Model ◽

Dynamic Network Models ◽

Efficient Information ◽

The Stability

Dynamic network is an abstraction of networks with frequent topology changes arising from node mobility or other reasons. In this article, we first propose a dynamic network model, named ( T, L)-HiNet, to extend existing dynamic network models with clusters. ( T, L)-HiNet includes several properties defining the stability of cluster hierarchy in a dynamic network, including cluster head set, cluster member set and the connections among them. Based on ( T, L)-HiNet, we design several hierarchical information dissemination algorithms for different scenarios of dynamics. Furthermore, we extend the ( T, L)-HiNet model and corresponding algorithms in two directions, that is, stability of cluster head set and cluster size. The correctness of our algorithms is proved rigorously, and their performance is evaluated via both numerical analysis and simulations. The results show that compared with the algorithm recently proposed by Kuhn et al., our design can significantly reduce communication cost and also time cost.

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