Factorial graphical models for dynamic networks

2015 ◽  
Vol 3 (1) ◽  
pp. 37-57 ◽  
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.

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

Information ◽  
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.

scholarly journals Affect and Personality

2020 ◽  
Vol 36 (6) ◽  
pp. 1009-1023
Author(s):  
Jonathan J. Park ◽  
Sy-Miin Chow ◽  
Zachary F. Fisher ◽  
Peter C. M. Molenaar
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Var Models ◽  
Vector Autoregressive ◽  
Individual Level ◽  
Dynamic Network Models ◽  
Standard Vector ◽  
Personality Theories ◽  
Network Approaches

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.

scholarly journals Cluster-based efficient information dissemination in dynamic networks

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.

scholarly journals Network Topology Impact on the Identification of Dynamic Network Models with Application to Autonomous Vehicle Platooning

2020 ◽  
Vol 53 (2) ◽  
pp. 1031-1036
Author(s):  
Guilherme A. Pimentel ◽  
Rafael de Vasconcelos ◽  
Aurélio Salton ◽  
Alexandre Bazanella
Keyword(s):  
Network Topology ◽  
Autonomous Vehicle ◽  
Dynamic Network ◽  
Network Models ◽  
Dynamic Network Models ◽  
Vehicle Platooning

scholarly journals A review of dynamic network models with latent variables

2018 ◽  
Vol 12 (0) ◽  
pp. 105-135 ◽  
Author(s):  
Bomin Kim ◽  
Kevin H. Lee ◽  
Lingzhou Xue ◽  
Xiaoyue Niu
Keyword(s):  
Latent Variables ◽  
Dynamic Network ◽  
Network Models ◽  
Dynamic Network Models

Reconstruction of dynamic network models from metabolite measurements

2007 ◽  
pp. 97-127 ◽  
Author(s):  
Matthias Reuss ◽  
Luciano Aguilera-Vázquez ◽  
Klaus Mauch
Keyword(s):  
Dynamic Network ◽  
Network Models ◽  
Dynamic Network Models

scholarly journals Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

2018 ◽  
Vol 373 (1758) ◽  
pp. 20170377 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Caenorhabditis Elegans ◽  
Graphical Models ◽  
Neuronal Network ◽  
Graphical Model ◽  
Time Series Data ◽  
Series Data ◽  
Neuronal Responses ◽  
C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

An experimental evaluation and guideline for path finding in weighted dynamic network

2021 ◽  
Vol 14 (11) ◽  
pp. 2127-2140
Author(s):  
Mengxuan Zhang ◽  
Lei Li ◽  
Xiaofang Zhou
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Real Life ◽  
Dynamic Network ◽  
Problem Space ◽  
Network Applications ◽  
Complete Problem ◽  
Path Computation ◽  
Shortest Path Computation

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the Dynamic Shortest Path (DSP) problem has drawn lots of attention in recent years. However, as DSP has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the determinant dimensions and constraint dimensions of the DSP problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art DSP methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of dynamic degree to classify the dynamic environments and use throughput to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.

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