Search results for "Dynamic network analysis"

Community driven dynamics of oscillatory network responses to threat

2019

AbstractPhysiological responses to threat stimuli involve neural synchronized oscillations in cerebral networks with distinct organization properties. Community architecture within these networks and its dynamic adaptation could play a critical role in achieving optimal physiological responses.Here we applied dynamic network analyses to address the early phases of threat processing at the millisecond level, describing multi-frequency (theta and alpha) integration and basic reorganization properties (flexibility and clustering) that drive physiological responses. We quantified cortical and subcortical network interactions and captured illustrative reconfigurations using community allegiance …

Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain

2018

Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the networks, even the event-related ones, are highly heterogeneous due to spurious interactions, which bring challenges to revealing the change patterns of interactive information in the complex dynamic process. In this paper, we propose a network entropy (NE) method to measure connectivity uncertainty of FCGB sequences to alleviate the spurious interaction problem in dynamic network analysis to realize associations with different events during a complex cognitive task. The p…

Optimal Usage of Multiple Network Connections

2008

In the future mobile networks, a mobile terminal is able to select the best suitable network for each data transmission. The selection of a network connection to be used has been under a lot of study. In this paper, we consider a more extensive case in which we do not select a network connection but use several network connections simultaneously to transfer data. When data is transferred using multiple network connections, a network connection has to be selected for each component of the data. We have modelled this problem as a multiobjective optimization problem and developed a heuristic to solve the problem fast in a static network environment. In this paper, we discuss solving the proble…

Inferring slowly-changing dynamic gene-regulatory networks

2015

Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experi…

Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination

2012

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often…

Factorial graphical models for dynamic networks

2015

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 inte…

Recent Advances in Complex Networks Theories with Applications

2014

Attracting sets in network regulatory theory

2016

Modern telecommunication networks are very complex and they should be able to deal with rapid and unpredictable changes in traffic flows. Virtual Network Topology is used to carry IP traffic over Wavelength-division multiplexing optical network. To use network resources in the most optimal way, there is a need for an algorithm, which will dynamically re-share resources among all devices in the particular network segment, based on links utilization between routers. Attractor selection mechanism could be used to dynamically control such Virtual Network Topology. The advantage of this algorithm is that it can adopt to very rapid, unknown and unpredictable changes in traffic flows. This mechani…

Dynamic network identification from non-stationary vector autoregressive time series

2018

Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individu…

Overlapping Community Structure in Co-authorship Networks: A Case Study

2014

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological prop…